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Kırboğa KK, Işık M. Explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting. J Comput Chem 2024; 45:1530-1539. [PMID: 38491535 DOI: 10.1002/jcc.27335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 03/18/2024]
Abstract
Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC50 values, and their efficacy was gauged using metrics including R2, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (R2: 0.93, RMSE: 0.43, time-taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(O)N) and PubChemFP6978 (C(O)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(O)NCC), PubChemFP601 (C(O)OCC), and PubChemFP432 (C(O)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(O)OCCN), PubChemFP791 (C(O)OCCC), PubChemFP696 (C(O)OCCCC), PubChemFP335 (C(O)NCCN), PubChemFP580 (C(O)NCCCN), and PubChemFP180 (C(O)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC50 values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
- Bioengineering Department, Süleyman Demirel University, Isparta, Turkey
| | - Mesut Işık
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
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2
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Zhang R, Nolte D, Sanchez-Villalobos C, Ghosh S, Pal R. Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling. Nat Commun 2024; 15:5072. [PMID: 38871711 DOI: 10.1038/s41467-024-49372-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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Affiliation(s)
- Ruibo Zhang
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Daniel Nolte
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Cesar Sanchez-Villalobos
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NB, 68588, USA.
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
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3
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Schwaebe B, He H, Glaubensklee C, Ogunseitan OA, Schoenung JM. Chemical hazard assessment toward safer electrolytes for lithium-ion batteries. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024. [PMID: 38837720 DOI: 10.1002/ieam.4963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/16/2024] [Accepted: 04/29/2024] [Indexed: 06/07/2024]
Abstract
Commercialization of rechargeable lithium-ion (Li-ion) batteries has revolutionized the design of portable electronic devices and is facilitating the current transition to electric vehicles. The technological specifications of Li-ion batteries continue to evolve through the introduction of various high-risk liquid electrolyte chemicals, yet critical evaluation of the physical, environmental, and human health hazards of these substances is lacking. Using the GreenScreen for Safer Chemicals approach, we conducted a chemical hazard assessment (CHA) of 103 electrolyte chemicals categorized into seven chemical groups: salts, carbonates, esters, ethers, sulfoxides-sulfites-sulfones, overcharge protection additives, and flame-retardant additives. To minimize data gaps, we focused on six toxicity and hazard data sources, including three empirical and three nonempirical predictive data sources. Furthermore, we investigated the structural similarities among selected electrolyte chemicals using the ChemMine tool and the simplified molecular input line entry system inputs from PubChem to evaluate whether chemicals with similar structures exhibit similar toxicity. The results demonstrate that salts, overcharge protection additives, and flame-retardant additives contain the most toxic components in the electrolyte solutions. Furthermore, carbonates, esters, and ethers account for most flammability hazards in Li-ion batteries. This study supports the complementary use of quantitative structure-activity relationship models to minimize data gaps and inconsistencies in CHA. Integr Environ Assess Manag 2024;00:1-14. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Branden Schwaebe
- Department of Materials Science and Engineering, University of California, Irvine, California, USA
| | - Haoyang He
- Department of Materials Science and Engineering, University of California, Irvine, California, USA
| | - Christopher Glaubensklee
- Department of Materials Science and Engineering, University of California, Irvine, California, USA
| | - Oladele A Ogunseitan
- Department of Population Health and Disease Prevention, University of California Irvine, Irvine, California, USA
- World Institute for Sustainable Development of Materials (WISDOM), University of California, Irvine, California, USA
| | - Julie M Schoenung
- Department of Materials Science and Engineering, University of California, Irvine, California, USA
- World Institute for Sustainable Development of Materials (WISDOM), University of California, Irvine, California, USA
- Department of Materials Science & Engineering, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
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Zell L, Hofer TS, Schubert M, Popoff A, Höll A, Marschhofer M, Huber-Cantonati P, Temml V, Schuster D. Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction - A case study. Biochem Pharmacol 2024:116340. [PMID: 38848779 DOI: 10.1016/j.bcp.2024.116340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/10/2024] [Accepted: 06/04/2024] [Indexed: 06/09/2024]
Abstract
The octanol-water distribution coefficient (logP), used as a measure of lipophilicity, plays a major role in the drug design and discovery processes. While average logP values remain unchanged in approved oral drugs since 1983, current medicinal chemistry trends towards increasingly lipophilic compounds that require adapted analytical workflows and drug delivery systems. Solubility enhancers like cyclodextrins (CDs), especially 2-hydroxypropyl-β-CD (2-HP-β-CD), have been studied in vitro and in vivo investigating their ADMET (adsorption, distribution, metabolism, excretion and toxicity)-related properties. However, data is scarce regarding the applicability of CD inclusion complexes (ICs) in vitro compared to pure compounds. In this study, dopamine receptor (DR) ligands were used as a case study, utilizing a combined in silico/in vitro workflow. Media-dependent solubility and IC stoichiometry were investigated using HPLC. NMR was used to observe IC formation-caused chemical shift deviations while in silico approaches utilizing basin hopping global minimization were used to propose putative IC binding modes. A cell-based in vitro homogeneously time-resolved fluorescence (HTRF) assay was used to quantify ligand binding affinity at the DR subtype 2 (D2R). While all ligands showed increased solubility using 2-HP-β-CD, they differed regarding IC stoichiometry and receptor binding affinity. This case study shows that IC-formation was ligand-dependent and sometimes altering in vitro binding. Therefore, IC complex formation can't be recommended as a general means of improving compound solubility for in vitro studies as they may alter ligand binding.
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Affiliation(s)
- Lukas Zell
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Thomas S Hofer
- Institute of General, Inorganic and Theoretical Chemistry, Center for Biochemistry and Biomedicine, University of Innsbruck, 6020 Innsbruck, Austria
| | - Mario Schubert
- Department of Biosciences and Medical Biology, University of Salzburg, 5020 Salzburg, Austria; Department of Chemistry, Freie Universität Berlin, 14195 Berlin, Germany
| | - Alexander Popoff
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Anna Höll
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Moritz Marschhofer
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Petra Huber-Cantonati
- Department of Pharmaceutical Biology, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Veronika Temml
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Daniela Schuster
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, 5020 Salzburg, Austria.
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Milon TI, Wang Y, Fontenot RL, Khajouie P, Villinger F, Raghavan V, Xu W. Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions. Comput Biol Chem 2024; 112:108117. [PMID: 38852360 DOI: 10.1016/j.compbiolchem.2024.108117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/13/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.
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Affiliation(s)
- Tarikul I Milon
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Yuhong Wang
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ryan L Fontenot
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Poorya Khajouie
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Francois Villinger
- Department of Biology, University of Louisiana at Lafayette, New Iberia, LA 70560, USA
| | - Vijay Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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Zhou Y, Wang Z, Huang Z, Li W, Chen Y, Yu X, Tang Y, Liu G. In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches. J Appl Toxicol 2024; 44:892-907. [PMID: 38329145 DOI: 10.1002/jat.4586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/09/2024]
Abstract
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.
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Affiliation(s)
- Yiqing Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yuanting Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xinxin Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Kaboudi N, Asl SG, Nourani N, Shayanfar A. Solubilization of drugs using beta-cyclodextrin: Experimental data and modeling. ANNALES PHARMACEUTIQUES FRANÇAISES 2024; 82:663-672. [PMID: 38340807 DOI: 10.1016/j.pharma.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Many drug candidates fail to complete the entire drug development process because of poor physicochemical properties. Solubility is an important physicochemical property which plays a vital role in various stages of drug discovery and development. Several methods have been proposed to enhance the solubility of drugs, and complex formation with cyclodextrins is among them. Beta-cyclodextrin (βCD) is a common excipient for solubilization of drugs. The aim of this study is to develop the mechanistic QSPR models to predict the solubility enhancement of a drug in the presence of βCD. In this study, the solubility enhancement of some drugs in the presence of 10mM βCD at 25°C was experimentally determined or collected from the literature. Two different models to predict the solubilization by βCD were developed by binary logistic regression using structural properties of drugs with more than 80% accuracy. Polar surface area and excess molar refraction are the main parameters for estimating solubilization by βCD. Moreover, other descriptors related to hydrophobicity and the capability of hydrogen bonding formation of molecules could improve the accuracy of the established models.
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Affiliation(s)
- Navid Kaboudi
- Student Research Committee, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Saba Ghasemi Asl
- Student Research Committee, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nasim Nourani
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Shayanfar
- Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
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Bessa CDPB, Feu AE, de Menezes RPB, Scotti MT, Lima JMG, Lima ML, Tempone AG, de Andrade JP, Bastida J, Borges WDS. Multitarget anti-parasitic activities of isoquinoline alkaloids isolated from Hippeastrum aulicum (Amaryllidaceae). PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155414. [PMID: 38503155 DOI: 10.1016/j.phymed.2024.155414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/02/2024] [Accepted: 02/03/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Chagas disease and leishmaniasis affect a significant portion of the Latin American population and still lack efficient treatments. In this context, natural products emerge as promising compounds for developing more effective therapies, aiming to mitigate side effects and drug resistance. Notably, species from the Amaryllidaceae family emerge as potential reservoirs of antiparasitic agents due to the presence of diverse biologically active alkaloids. PURPOSE To assess the anti-Trypanosoma cruzi and anti-Leishmania infantum activity of five isolated alkaloids from Hippeastrum aulicum Herb. (Amaryllidaceae) against different life stages of the parasites using in silico and in vitro assays. Furthermore, molecular docking was employed to evaluate the interaction of the most active alkaloids. METHODS Five natural isoquinoline alkaloids isolated in suitable quantities for in vitro testing underwent preliminary in silico analysis to predict their potential efficacy against Trypanosoma cruzi (amastigote and trypomastigote forms) and Leishmania infantum (amastigote and promastigote forms). The in vitro antiparasitic activity and mammalian cytotoxicity were investigated with a subsequent comparison of both analysis (in silico and in vitro) findings. Additionally, this study employed the molecular docking technique, utilizing cruzain (T. cruzi) and sterol 14α-demethylase (CYP51, L. infantum) as crucial biological targets for parasite survival, specifically focusing on compounds that exhibited promising activities against both parasites. RESULTS Through computational techniques, it was identified that the alkaloids haemanthamine (1) and lycorine (8) were the most active against T. cruzi (amastigote and trypomastigote) and L. infantum (amastigote and promastigote), while also revealing unprecedented activity of alkaloid 7‑methoxy-O-methyllycorenine (6). The in vitro analysis confirmed the in silico tests, in which compound 1 presented the best activities against the promastigote and amastigote forms of L. infantum with half-maximal inhibitory concentration (IC50) 0.6 µM and 1.78 µM, respectively. Compound 8 exhibited significant activity against the amastigote form of T. cruzi (IC50 7.70 µM), and compound 6 demonstrated activity against the trypomastigote forms of T. cruzi and amastigote of L. infantum, with IC50 values of 89.55 and 86.12 µM, respectively. Molecular docking analyses indicated that alkaloids 1 and 8 exhibited superior interaction energies compared to the inhibitors. CONCLUSION The hitherto unreported potential of compound 6 against T. cruzi trypomastigotes and L. infantum amastigotes is now brought to the forefront. Furthermore, the acquired dataset signifies that the isolated alkaloids 1 and 8 from H. aulicum might serve as prototypes for subsequent structural refinements aimed at the exploration of novel leads against both T. cruzi and L. infantum parasites.
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Affiliation(s)
- Carliani Dal Piero Betzel Bessa
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil
| | - Amanda Eiriz Feu
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil
| | - Renata Priscila Barros de Menezes
- Programa de Pós-graduação em Produtos Naturais e Sintéticos Bioativos (PgPNSB), Universidade Federal da Paraíba, Campus I, Cidade Universitária, João Pessoa 58051-900, Brazil
| | - Marcus Tullius Scotti
- Programa de Pós-graduação em Produtos Naturais e Sintéticos Bioativos (PgPNSB), Universidade Federal da Paraíba, Campus I, Cidade Universitária, João Pessoa 58051-900, Brazil
| | | | - Marta Lopes Lima
- School of Life Sciences, University of Dundee, Scotland DD1 4HN, United Kingdom
| | | | - Jean Paulo de Andrade
- Departamento de Medicina Traslacional, Facultad de Medicina, Escuela de Química y Farmacia, Universidad Católica del Maule, Talca 3480112, Chile
| | - Jaume Bastida
- Departament de Biologia, Sanitat i Medi Ambient, Facultat de Farmàcia i Ciències de l´Alimentació, Universitat de Barcelona, Barcelona 08028, Spain
| | - Warley de Souza Borges
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil.
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Wang J, Wang P, Liu B, Kinney PL, Huang L, Chen K. Comprehensive evaluation framework for intervention on health effects of ambient temperature. ECO-ENVIRONMENT & HEALTH 2024; 3:154-164. [PMID: 38646097 PMCID: PMC11031729 DOI: 10.1016/j.eehl.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/28/2023] [Accepted: 01/12/2024] [Indexed: 04/23/2024]
Abstract
Despite the existence of many interventions to mitigate or adapt to the health effects of climate change, their effectiveness remains unclear. Here, we introduce the Comprehensive Evaluation Framework for Intervention on Health Effects of Ambient Temperature to evaluate study designs and effects of intervention studies. The framework comprises three types of interventions: proactive, indirect, and direct, and four categories of indicators: classification, methods, scope, and effects. We trialed the framework by an evaluation of existing intervention studies. The evaluation revealed that each intervention has its own applicable characteristics in terms of effectiveness, feasibility, and generalizability scores. We expanded the framework's potential by offering a list of intervention recommendations in different scenarios. Future applications are then explored to establish models of the relationship between study designs and intervention effects, facilitating effective interventions to address the health effects of ambient temperature under climate change.
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Affiliation(s)
- Jiaming Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Peng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China
| | - Beibei Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Patrick L. Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Lei Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Center for Public Health Research, Medical School of Nanjing University, Nanjing 210093, China
| | - Kai Chen
- Department of Environmental Health Sciences, Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT 06510, USA
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Kehrein J, Bunker A, Luxenhofer R. POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles. Mol Pharm 2024. [PMID: 38805643 DOI: 10.1021/acs.molpharmaceut.4c00086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.
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Affiliation(s)
- Josef Kehrein
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Robert Luxenhofer
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
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Camargo PG, Dos Santos CR, Girão Albuquerque M, Rangel Rodrigues C, Lima CHDS. Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors. Sci Rep 2024; 14:11575. [PMID: 38773273 PMCID: PMC11109165 DOI: 10.1038/s41598-024-62520-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
Leishmaniasis is a disease caused by a protozoan of the genus Leishmania, affecting millions of people, mainly in tropical countries, due to poor social conditions and low economic development. First-line chemotherapeutic agents involve highly toxic pentavalent antimonials, while treatment failure is mainly due to the emergence of drug-resistant strains. Leishmania arginase (ARG) enzyme is vital in pathogenicity and contributes to a higher infection rate, thus representing a potential drug target. This study helps in designing ARG inhibitors for the treatment of leishmaniasis. Py-CoMFA (3D-QSAR) models were constructed using 34 inhibitors from different chemical classes against ARG from L. (L.) amazonensis (LaARG). The 3D-QSAR predictions showed an excellent correlation between experimental and calculated pIC50 values. The molecular docking study identified the favorable hydrophobicity contribution of phenyl and cyclohexyl groups as substituents in the enzyme allosteric site. Molecular dynamics simulations of selected protein-ligand complexes were conducted to understand derivatives' interaction modes and affinity in both active and allosteric sites. Two cinnamide compounds, 7g and 7k, were identified, with similar structures to the reference 4h allosteric site inhibitor. These compounds can guide the development of more effective arginase inhibitors as potential antileishmanial drugs.
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Affiliation(s)
- Priscila Goes Camargo
- Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Carine Ribeiro Dos Santos
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Magaly Girão Albuquerque
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Carlos Rangel Rodrigues
- Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - Camilo Henrique da Silva Lima
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
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12
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Walter M, Webb SJ, Gillet VJ. Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features. J Chem Inf Model 2024; 64:3670-3688. [PMID: 38686880 PMCID: PMC11094726 DOI: 10.1021/acs.jcim.4c00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds. While these methods have produced promising results in some cases, they do not shed light on how representations of compounds are transformed in hidden layers, which constitute how neural networks learn. We present a novel technique to interpret neural networks which identifies chemical substructures in training data found to be responsible for the activation of hidden neurons. For individual test compounds, the importance of hidden neurons is determined, and the associated substructures are leveraged to explain the model prediction. Using structural alerts for mutagenicity from the Derek Nexus expert system as ground truth, we demonstrate the validity of the approach and show that model explanations are competitive with and complementary to explanations obtained from an established feature attribution method.
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Affiliation(s)
- Moritz Walter
- Information
School, University of Sheffield, The Wave, 2 Whitham Road, Sheffield S10 2AH, U.K.
| | - Samuel J. Webb
- Lhasa
Limited, Granary Wharf
House, 2 Canal Wharf, Leeds LS11 5PY, U.K.
| | - Valerie J. Gillet
- Information
School, University of Sheffield, The Wave, 2 Whitham Road, Sheffield S10 2AH, U.K.
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13
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Burger PB, Hu X, Balabin I, Muller M, Stanley M, Joubert F, Kaiser TM. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology. J Chem Inf Model 2024; 64:3812-3825. [PMID: 38651738 PMCID: PMC11094716 DOI: 10.1021/acs.jcim.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
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Affiliation(s)
- Pieter B. Burger
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Xiaohu Hu
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ilya Balabin
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Morné Muller
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Megan Stanley
- Microsoft
Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K.
| | - Fourie Joubert
- Centre
for Bioinformatics and Computational Biology, Department of Biochemistry,
Genetics and Microbiology, University of
Pretoria, Pretoria 0001, South Africa
| | - Thomas M. Kaiser
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
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14
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Ni B, Wang H, Khalaf HKS, Blay V, Houston DR. AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening. J Chem Inf Model 2024; 64:3779-3789. [PMID: 38624083 PMCID: PMC11094722 DOI: 10.1021/acs.jcim.4c00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.
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Affiliation(s)
- Boyang Ni
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Haoying Wang
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Huda Kadhim Salem Khalaf
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Vincent Blay
- Department
of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, California 95064, United States
| | - Douglas R. Houston
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
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15
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Hosseini MAH, Alizadeh AA, Shayanfar A. Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties. Eur J Drug Metab Pharmacokinet 2024:10.1007/s13318-024-00892-6. [PMID: 38733548 DOI: 10.1007/s13318-024-00892-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND OBJECTIVE The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug. METHODS A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression. RESULTS Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize. CONCLUSION A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.
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Affiliation(s)
- Mir Amir Hossein Hosseini
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Clinical Pharmacy, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Akbar Alizadeh
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Shayanfar
- Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
- Faculty of Pharmacy, Tabriz University of Medical Sciences, Golgasht St., Tabriz, 51664-14766, Iran.
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16
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Lovrić M, Wang T, Staffe MR, Šunić I, Časni K, Lasky-Su J, Chawes B, Rasmussen MA. A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation. Metabolites 2024; 14:278. [PMID: 38786755 PMCID: PMC11122766 DOI: 10.3390/metabo14050278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 04/29/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children's serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.
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Affiliation(s)
- Mario Lovrić
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia;
- The Lisbon Council, 1040 Brussels, Belgium
| | - Tingting Wang
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark
| | - Mads Rønnow Staffe
- Department of Food Science, University of Copenhagen, 1958 Frederiksberg, Denmark
| | - Iva Šunić
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia;
| | | | - Jessica Lasky-Su
- Department of Medicine, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2300 Copenhagen, Denmark
| | - Morten Arendt Rasmussen
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark
- Department of Food Science, University of Copenhagen, 1958 Frederiksberg, Denmark
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17
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Yang Z, Huang T, Pan L, Wang J, Wang L, Ding J, Xiao J. QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning. J Cheminform 2024; 16:48. [PMID: 38685101 PMCID: PMC11059686 DOI: 10.1186/s13321-024-00843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024] Open
Abstract
Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .
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Affiliation(s)
- Zhijiang Yang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Tengxin Huang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Li Pan
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Jingjing Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junhua Xiao
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
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18
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Kim K, Jang A, Shin H, Ye I, Lee JE, Kim T, Park H, Hong S. Concurrent Optimizations of Efficacy and Blood-Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson's Disease Therapeutics. J Med Chem 2024. [PMID: 38684226 DOI: 10.1021/acs.jmedchem.4c00520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.
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Affiliation(s)
- Kewon Kim
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Center for Catalytic Hydrocarbon Functionalizations, Institute for Basic Science (IBS), Daejeon 34141, Korea
| | - Ahyoung Jang
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Center for Catalytic Hydrocarbon Functionalizations, Institute for Basic Science (IBS), Daejeon 34141, Korea
| | - Hochul Shin
- Whan In Pharmaceutical Co., Ltd., 11, Beobwon-ro 6-gil, Songpa-gu, Seoul 05855, Korea
| | - Inhae Ye
- Whan In Pharmaceutical Co., Ltd., 11, Beobwon-ro 6-gil, Songpa-gu, Seoul 05855, Korea
| | - Ji Eun Lee
- Whan In Pharmaceutical Co., Ltd., 11, Beobwon-ro 6-gil, Songpa-gu, Seoul 05855, Korea
| | - Taeho Kim
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Korea
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Korea
| | - Sungwoo Hong
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Center for Catalytic Hydrocarbon Functionalizations, Institute for Basic Science (IBS), Daejeon 34141, Korea
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19
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Song L, Zhu H, Wang K, Li M. LGGA-MPP: Local Geometry-Guided Graph Attention for Molecular Property Prediction. J Chem Inf Model 2024; 64:3105-3113. [PMID: 38516950 DOI: 10.1021/acs.jcim.3c02058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Molecular property prediction is a fundamental task of drug discovery. With the rapid development of deep learning, computational approaches for predicting molecular properties are experiencing increasing popularity. However, these existing methods often ignore the 3D information on molecules, which is critical in molecular representation learning. In the past few years, several self-supervised learning (SSL) approaches have been proposed to exploit the geometric information by using pre-training on 3D molecular graphs and fine-tuning on 2D molecular graphs. Most of these approaches are based on the global geometry of molecules, and there is still a challenge in capturing the local structure and local interpretability. To this end, we propose local geometry-guided graph attention (LGGA), which integrates local geometry into the attention mechanism and message-passing of graph neural networks (GNNs). LGGA introduces a novel method to model molecules, enhancing the model's ability to capture intricate local structural details. Experiments on various data sets demonstrate that the integration of local geometry has a significant impact on the improved results, and our model outperforms the state-of-the-art methods for molecular property prediction, establishing its potential as a promising tool in drug discovery and related fields.
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Affiliation(s)
- Lei Song
- School of Software, XinJiang University, Urumqi 830091, China
| | - Huimin Zhu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Kaili Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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20
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Fan J, Shi S, Xiang H, Fu L, Duan Y, Cao D, Lu H. Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods. J Chem Inf Model 2024; 64:3080-3092. [PMID: 38563433 DOI: 10.1021/acs.jcim.3c02030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.
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Affiliation(s)
- Jianing Fan
- Health Management Center, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Department of Cardiology, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
| | - Shaohua Shi
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong 999077, P. R. China
| | - Hong Xiang
- Center for Experimental Medicine, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
| | - Yanjing Duan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan P. R. China
| | - Hongwei Lu
- Health Management Center, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Department of Cardiology, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Center for Experimental Medicine, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
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21
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Aminu KS, Uzairu A, Chandra A, Singh N, Abechi SE, Shallangwa GA, Umar AB. Exploring the potential of 2-arylbenzimidazole scaffolds as novel α-amylase inhibitors: QSAR, molecular docking, simulation and pharmacokinetic studies. In Silico Pharmacol 2024; 12:29. [PMID: 38617707 PMCID: PMC11009192 DOI: 10.1007/s40203-024-00205-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 03/13/2024] [Indexed: 04/16/2024] Open
Abstract
Previous studies have shown that 2-arylbenzimidazole derivatives have a strong anti-diabetic effect. To further explore this potential, we develop new analogues of the compound using ligand-based drug design and tested their inhibitory and binding properties through QSAR analyses, molecular docking, dynamic simulations and pharmacokinetic studies. By using quantitative structure activity relationship and ligand-based modification, a highly precise predictive model and design of potent compounds was developed from the derivatives of 2-arylbenzimidazoles. Molecular docking and simulation studies were then conducted to identify the optimal binding poses and pharmacokinetic profiles of the newly generated therapeutic drugs. DFT was employed to optimize the chemical structures of 2-arylbenzimidazole derivatives using B3LYP/6-31G* as the basis set. The model with the highest R2trng set, R2adj, Q2cv, and R2test sets (0.926, 0.912, 0.903, and 0.709 respectively) was chosen to predict the inhibitory activities of the derivatives. Five analogues designed using ligand-based strategy had higher activity than the hit molecule. Additionally, the designed molecules had more favorable MolDock scores than the hit molecule and acarbose and simulation studies confirm on their stability and binding affinities towards the protein. The ADME and druglikeness properties of the analogues indicated that they are safe to consume orally and have a high potential for total clearance. The results of this study showed that the suggested analogues could act as α-amylase inhibitors, which could be used as a basis for the creation of new drugs to treat type 2 diabetes mellitus.
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Affiliation(s)
- Khalifa Sunusi Aminu
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
- Department of Pure and Industrial Chemistry, Bayero University, Kano, Nigeria
| | - Adamu Uzairu
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
| | - Anshuman Chandra
- School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Nagendra Singh
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
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22
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Masand VH, Al-Hussain SA, Alzahrani AY, Al-Mutairi AA, Hussien RA, Samad A, Zaki MEA. Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures. ACS OMEGA 2024; 9:16759-16774. [PMID: 38617692 PMCID: PMC11007693 DOI: 10.1021/acsomega.4c00906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 04/16/2024]
Abstract
Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India
| | - Sami A Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Abdullah Y Alzahrani
- Department of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail 61421, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Rania A Hussien
- Department of Chemistry, Faculty of Science, Al-Baha University, Al-Baha 65799, Kingdom of Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Magdi E A Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
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23
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Wang K, Kim N, Bagherian M, Li K, Chou E, Colacino JA, Dolinoy DC, Sartor MA. Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5889-5898. [PMID: 38501580 PMCID: PMC11131040 DOI: 10.1021/acs.est.4c00458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.
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Affiliation(s)
- Kai Wang
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nicole Kim
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
| | - Kai Li
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elysia Chou
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Justin A. Colacino
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dana C. Dolinoy
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maureen A. Sartor
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
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24
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Das AP, Agarwal SM. Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches. Mol Divers 2024; 28:901-925. [PMID: 36670282 PMCID: PMC9859751 DOI: 10.1007/s11030-022-10590-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/18/2022] [Indexed: 01/22/2023]
Abstract
Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.
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Affiliation(s)
- Agneesh Pratim Das
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India.
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25
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Gustavsson M, Käll S, Svedberg P, Inda-Diaz JS, Molander S, Coria J, Backhaus T, Kristiansson E. Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms. SCIENCE ADVANCES 2024; 10:eadk6669. [PMID: 38446886 PMCID: PMC10917336 DOI: 10.1126/sciadv.adk6669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/30/2024] [Indexed: 03/08/2024]
Abstract
Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.
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Affiliation(s)
- Mikael Gustavsson
- Department of Economics, University of Gothenburg, Gothenburg, Sweden
| | - Styrbjörn Käll
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
| | - Patrik Svedberg
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Juan S. Inda-Diaz
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
| | - Sverker Molander
- Division of Environmental Systems Analysis, Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden
| | - Jessica Coria
- Department of Economics, University of Gothenburg, Gothenburg, Sweden
| | - Thomas Backhaus
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Erik Kristiansson
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
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26
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Erickson M, Casañola-Martin G, Han Y, Rasulev B, Kilin D. Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. J Phys Chem B 2024; 128:2190-2200. [PMID: 38386478 DOI: 10.1021/acs.jpcb.3c06854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Gerardo Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
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27
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Huang Z, Yu J, He W, Yu J, Deng S, Yang C, Zhu W, Shao X. AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133355. [PMID: 38198864 DOI: 10.1016/j.jhazmat.2023.133355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
The development of accurate and interpretable models for predicting reaction constants of organic compounds with hydroxyl radicals is vital for advancing quantitative structure-activity relationships (QSAR) in pollutant degradation. Methods like molecular descriptors, molecular fingerprinting, and group contribution methods have limitations, as traditional machine learning struggles to capture all intramolecular information simultaneously. To address this, we established an integrated graph neural network (GNN) with approximately 12 million learnable parameters. GNN represents atoms as nodes and chemical bonds as edges, thus transforming molecules into a graph structures, effectively capturing microscopic properties while depicting atom connectivity in non-Euclidean space. Our datasets comprise 1401 pollutants to develop an integrated GNN model with Bayesian optimization, the model achieves root mean square errors of 0.165, 0.172, and 0.189 on the training, validation, and test datasets, respectively. Furthermore, we assess molecular structure similarity using molecular fingerprint to enhance the model's applicability. Afterwards, we propose a gradient weight mapping method for model explainability, uncovering the key functional groups in chemical reactions in artificial intelligence perspective, which would boost chemistry through artificial intelligence extreme arithmetic power.
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Affiliation(s)
- Zhi Huang
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China
| | - Jiang Yu
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China; Institute of New Energy and Low Carbon Technology, Sichuan University, Chengdu 610065, PR China; Yibin Institute of Industrial Technology, Sichuan University, Yibin 644000, PR China.
| | - Wei He
- Chengdu Jin Sheng Water Engineering Co, PR China
| | - Jie Yu
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China; Institute of New Energy and Low Carbon Technology, Sichuan University, Chengdu 610065, PR China
| | - Siwei Deng
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China
| | - Chun Yang
- Ministry of Education and School of Mathematics Sciences, Sichuan Normal University, PR China
| | - Weiwei Zhu
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China
| | - Xiao Shao
- School of Agriculture and Environment, University of Western Australia, Perth 6907, Western Australia, Australia
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28
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Vukomanović P, Stefanović M, Stevanović JM, Petrić A, Trenkić M, Andrejević L, Lazarević M, Sokolović D, Veselinović AM. Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability. Pharm Res 2024; 41:493-500. [PMID: 38337105 DOI: 10.1007/s11095-024-03675-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure-activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments. METHODS The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits. RESULTS A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed. CONCLUSION The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.
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Affiliation(s)
- Predrag Vukomanović
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Gynecology and Obstetrics, University Clinical Centre of Niš, Niš, Serbia
| | - Milan Stefanović
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Gynecology and Obstetrics, University Clinical Centre of Niš, Niš, Serbia
| | - Jelena Milošević Stevanović
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Gynecology and Obstetrics, University Clinical Centre of Niš, Niš, Serbia
| | - Aleksandra Petrić
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Gynecology and Obstetrics, University Clinical Centre of Niš, Niš, Serbia
| | - Milan Trenkić
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Gynecology and Obstetrics, University Clinical Centre of Niš, Niš, Serbia
| | - Lazar Andrejević
- COVID Hospital, University Clinical Centre of Niš, Kruševac, Serbia
| | - Milan Lazarević
- Faculty of Medicine, University of Niš, Niš, Serbia
- Clinic for Cardiovascular and Transplant Surgery, University Clinical Centre of Niš, Niš, Serbia
| | | | - Aleksandar M Veselinović
- Faculty of Medicine, Department of Chemistry, University of Niš, Bulevar Dr Zorana Đinđića 81, 18000, Niš, Serbia.
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29
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Ta GH, Weng CF, Leong MK. Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA. Toxicology 2024; 503:153739. [PMID: 38307191 DOI: 10.1016/j.tox.2024.153739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/22/2024] [Accepted: 01/28/2024] [Indexed: 02/04/2024]
Abstract
Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.
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Affiliation(s)
- Giang H Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan
| | - Ching-Feng Weng
- Institute of Respiratory Disease Department of Basic Medical Science Xiamen Medical College, Xiamen 361023, Fujian, China
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan.
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30
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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, Ghosh A, Masand VH, Ming LC, Rashid S. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain. J Biomol Struct Dyn 2024; 42:2550-2569. [PMID: 37144753 DOI: 10.1080/07391102.2023.2205948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/17/2023] [Indexed: 05/06/2023]
Abstract
Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata, India
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Vijay H Masand
- Department of Chemistry, Vidyabharati Mahavidyalalya, Amravati, Maharashtra, India
| | - Long Chiau Ming
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Nwadiugwu M, Onwuekwe I, Ezeanolue E, Deng H. Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles. Int J Mol Sci 2024; 25:2646. [PMID: 38473895 PMCID: PMC10931970 DOI: 10.3390/ijms25052646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 02/16/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
Current treatments for Alzheimer's disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3β (GSK-3β), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles-hallmarks of AD pathology. Leveraging quantitative structure-activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3β activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3β inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3β. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3β candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.
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Affiliation(s)
- Martin Nwadiugwu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Ikenna Onwuekwe
- Neurology Unit, Department of Medicine, University of Nigeria Teaching Hospital, Ituku-Ozalla 400001, Enugu, Nigeria;
- Department of Medicine, College of Medicine, University of Nigeria, Enugu Campus, Nsukka 400001, Enugu, Nigeria
| | - Echezona Ezeanolue
- Center for Translation and Implementation Research (CTAIR), University of Nigeria, Nsukka 410001, Enugu, Nigeria;
- Healthy Sunrise Foundation, Las Vegas, NV 89107, USA
| | - Hongwen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
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32
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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Masand V, Humane V, Mali S, Alzahrani AYA, Rashid S, Elossaily GM. Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in- vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor. J Biomol Struct Dyn 2024:1-31. [PMID: 38385447 DOI: 10.1080/07391102.2024.2319104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024]
Abstract
A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay Masand
- Department of Chemistry, Amravati, Maharashtra, India
| | - Vivek Humane
- Department of Chemistry, Shri R. R. Lahoti Science college, Morshi District: Amravati, Maharashtra, India
| | - Suraj Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Gehan M Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
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Sidorov P, Tsuji N. A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis. Chemistry 2024; 30:e202302837. [PMID: 38010242 DOI: 10.1002/chem.202302837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 11/29/2023]
Abstract
Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models.
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Affiliation(s)
- Pavel Sidorov
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan
| | - Nobuya Tsuji
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan
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Singh AV, Shelar A, Rai M, Laux P, Thakur M, Dosnkyi I, Santomauro G, Singh AK, Luch A, Patil R, Bill J. Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2835-2852. [PMID: 38315814 DOI: 10.1021/acs.jafc.3c06466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Amruta Shelar
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Mansi Rai
- Department of Microbiology, Central University of Rajasthan NH-8, Bandar Sindri, Dist-Ajmer-305817, Rajasthan, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Manali Thakur
- Uniklinik Köln, Kerpener Strasse 62, 50937 Köln Germany
| | - Ievgen Dosnkyi
- Institute of Chemistry and Biochemistry Department of Organic ChemistryFreie Universität Berlin Takustr. 3 14195 Berlin, Germany
| | - Giulia Santomauro
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
| | - Alok Kumar Singh
- Department of Plant Molecular Biology & Genetic Engineering, ANDUA&T, Ayodhya 224229, Uttar Pradesh, India
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Rajendra Patil
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Joachim Bill
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
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Mora JR, Marquez EA, Pérez-Pérez N, Contreras-Torres E, Perez-Castillo Y, Agüero-Chapin G, Martinez-Rios F, Marrero-Ponce Y, Barigye SJ. Rethinking the applicability domain analysis in QSAR models. J Comput Aided Mol Des 2024; 38:9. [PMID: 38351144 DOI: 10.1007/s10822-024-00550-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.
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Affiliation(s)
- Jose R Mora
- Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC- USFQ), Diego de Robles y Vía Interoceánica, Quito, 170901, Ecuador
| | - Edgar A Marquez
- Grupo de Investigaciones en Química Y Biología, Departamento de Química Y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla, 081007, Colombia
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Cátedras Conacyt, Ensenada, Baja California, México
| | - Noel Pérez-Pérez
- Colegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito (USFQ), Quito, Ecuador
| | - Ernesto Contreras-Torres
- Grupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17, Quito, 1200-841, Ecuador
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group, Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, 170504, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, Porto, 4450-208, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, Porto, 4169- 007, Portugal
| | - Felix Martinez-Rios
- Facultad de Ingeniería, Universidad Panamericana, CDMX, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México, 03920, México
| | - Yovani Marrero-Ponce
- Grupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17, Quito, 1200-841, Ecuador
- Facultad de Ingeniería, Universidad Panamericana, CDMX, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México, 03920, México
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), Madrid, 28049, Spain.
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Martinez-Mayorga K, Rosas-Jiménez JG, Gonzalez-Ponce K, López-López E, Neme A, Medina-Franco JL. The pursuit of accurate predictive models of the bioactivity of small molecules. Chem Sci 2024; 15:1938-1952. [PMID: 38332817 PMCID: PMC10848664 DOI: 10.1039/d3sc05534e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.
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Affiliation(s)
- Karina Martinez-Mayorga
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José G Rosas-Jiménez
- Department of Theoretical Biophysics, IMPRS on Cellular Biophysics Max-von-Laue Strasse 3 Frankfurt am Main 60438 Germany
| | - Karla Gonzalez-Ponce
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
| | - Edgar López-López
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute Mexico City 07000 Mexico
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
| | - Antonio Neme
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
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Cahill PL, Moodie LWK, Hertzer C, Pinori E, Pavia H, Hellio C, Brimble MA, Svenson J. Creating New Antifoulants Using the Tools and Tactics of Medicinal Chemistry. Acc Chem Res 2024; 57:399-412. [PMID: 38277792 DOI: 10.1021/acs.accounts.3c00733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
The unwanted accumulation of marine micro- and macroorganisms such as algae and barnacles on submerged man-made structures and vessel hulls is a major challenge for any marine operation. Known as biofouling, this problem leads to reduced hydrodynamic efficiency, significantly increased fuel usage, microbially induced corrosion, and, if not managed appropriately, eventual loss of both performance and structural integrity. Ship hull biofouling in the international maritime transport network conservatively accounts for 0.6% of global carbon emissions, highlighting the global scale and the importance of this problem. Improved antifouling strategies to limit surface colonization are paramount for essential activities such as shipping, aquaculture, desalination, and the marine renewable energy sector, representing both a multibillion dollar cost and a substantial practical challenge. From an ecological perspective, biofouling is a primary contributor to the global spread of invasive marine species, which has extensive implications for the marine environment.Historically, heavy metal-based toxic biocides have been used to control biofouling. However, their unwanted collateral ecological damage on nontarget species and bioaccumulation has led to recent global bans. With expanding human activities within aquaculture and offshore energy, it is both urgent and apparent that environmentally friendly surface protection remains key for maintaining the function of both moving and stationary marine structures. Biofouling communities are typically a highly complex network of both micro- and macroorganisms, representing a broad section of life from bacteria to macrophytes and animals. Given this diversity, it is unrealistic to expect that a single antifouling "silver bullet" will prevent colonization with the exception of generally toxic biocides. For that reason, modern and future antifouling solutions are anticipated to rely on novel coating technologies and "combination therapies" where mixtures of narrow-spectrum bioactive components are used to provide coverage across fouling species. In contrast to the existing cohort of outdated, toxic antifouling strategies, such as copper- and tributyltin-releasing paints, modern drug discovery techniques are increasingly being employed for the rational design of effective yet safe alternatives. The challenge for a medicinal chemistry approach is to effectively account for the large taxonomic diversity among fouling organisms combined with a lack of well-defined conserved molecular targets within most taxa.The current Account summarizes our work employing the tools of modern medicinal chemistry to discover, modify, and develop optimized and scalable antifouling solutions based on naturally occurring antifouling and repelling compounds from both marine and terrestrial sources. Inspiration for rational design comes from targeted studies on allelopathic natural products, natural repelling peptides, and secondary metabolites from sessile marine organisms with clean exteriors, which has yielded several efficient and promising antifouling leads.
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Affiliation(s)
- Patrick L Cahill
- Cawthron Institute, 98 Halifax Street East, Nelson 7010, New Zealand
| | - Lindon W K Moodie
- Drug Design and Discovery, Department of Medicinal Chemistry, Biomedical Centre, Uppsala University, 75123 Uppsala, Sweden
| | - Cora Hertzer
- Cawthron Institute, 98 Halifax Street East, Nelson 7010, New Zealand
| | - Emiliano Pinori
- RISE Research Institutes of Sweden, Division for Material and Production, 504 62 Borås, Sweden
| | - Henrik Pavia
- Department of Marine Sciences - Tjärnö, University of Gothenburg, SE-452 96 Strömstad, Sweden
| | - Claire Hellio
- Univ. Brest, Laboratoire des Sciences de l'Environnement MARin (LEMAR), CNRS, IRD, IFREMER, Brest 29285, France
| | - Margaret A Brimble
- School of Chemical Sciences, University of Auckland, 23 Symonds Street, Auckland 1010, New Zealand
| | - Johan Svenson
- Cawthron Institute, 98 Halifax Street East, Nelson 7010, New Zealand
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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39
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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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Kızılcan DŞ, Güzel Y, Türkmenoğlu B. Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure. Mol Divers 2024:10.1007/s11030-023-10798-1. [PMID: 38280974 DOI: 10.1007/s11030-023-10798-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 12/20/2023] [Indexed: 01/29/2024]
Abstract
The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.
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Affiliation(s)
- Dilek Şeyma Kızılcan
- Department of Chemistry, Faculty of Science, Erciyes University, Kayseri, Turkey
| | - Yahya Güzel
- Department of Chemistry, Faculty of Science, Erciyes University, Kayseri, Turkey
| | - Burçin Türkmenoğlu
- Department of Analytical Chemistry, Faculty of Pharmacy, Erzincan Binali Yıldırım University, Erzincan, Turkey.
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41
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Krishnan SR, Roy A, Gromiha MM. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief Bioinform 2024; 25:bbae002. [PMID: 38261341 PMCID: PMC10805179 DOI: 10.1093/bib/bbae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 01/24/2024] Open
Abstract
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.
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Affiliation(s)
- Sowmya R Krishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Department of Computer Science, National University of Singapore, Singapore 117543
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42
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Lee S, Ok SY, Moon HB, Seo SC, Ra JS. Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study. TOXICS 2024; 12:96. [PMID: 38276731 PMCID: PMC10818528 DOI: 10.3390/toxics12010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
This study introduces a novel concept approach for a read-across assessment, considering species sensitivity differences among phosphate chemicals within structurally similar compound groups. Twenty-five organic chemicals, with a log Kow of 5 or less, were categorized into three functional groups based on acetylcholinesterase (AChE) inhibition as a specific mode of action (MOA). The short-term aquatic toxicity data (LC50) for fish, crustaceans, and insects were collected from the U.S. EPA Ecotoxicology (ECOTOX) Knowledgebase. A geometric mean calculation method was applied for multiple toxic endpoints. Performance metrics for the new read-across concept, including correlation coefficient, bias, precision, and accuracy, were calculated. Overall, a slightly higher overestimation (49.2%) than underestimation (48.4%) in toxicity predictions was observed in two case studies. In Case study I, a strong positive correlation (r = 0.93) between the predicted and known toxicity values of target chemicals was observed, while in Case study II, with limited information on species and their ecotoxicity, showed a moderate correlation (r = 0.75). Overall, the bias and precision for Case study I were 0.32 ± 0.01, while Case study II showed 0.65 ± 0.06; however, the relative bias (%) increased from 37.65% (Case study I) to 91.94% (Case study II). Bland-Altman plots highlight the mean differences of 1.33 (Case study I) and 1.24 (Case study II), respectively. The new read-across concept, focusing on AChE inhibition and structural similarity, demonstrated good reliability, applicability, and accuracy with minimal bias. Future studies are needed to evaluate various types of chemical substances, diverse modes of action, functional groups, toxic endpoints, and test species to ensure overall comprehensiveness and robustness in toxicity predictions.
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Affiliation(s)
- Seokwon Lee
- Geum River Environment Research Center, National Institute of Environmental Research, Okcheon-gun 29027, Chungbuk, Republic of Korea;
| | - Seung-Yeop Ok
- Department of Environmental Fate and Modelling, Knoell Korea Ltd., Seoul 07327, Republic of Korea;
- Department of Marine Sciences and Convergent Engineering, Hanyang University, Ansan 15588, Republic of Korea;
| | - Hyo-Bang Moon
- Department of Marine Sciences and Convergent Engineering, Hanyang University, Ansan 15588, Republic of Korea;
| | - Sung-Chul Seo
- Department of Nano, Chemical and Biological Engineering, College of Engineering, Seokyeong University, Seoul 02173, Republic of Korea
| | - Jin-Sung Ra
- Regulatory Chemical Analysis & Risk Assessment Center, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Republic of Korea
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43
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Viganò EL, Ballabio D, Roncaglioni A. Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks. TOXICS 2024; 12:87. [PMID: 38276722 PMCID: PMC10820364 DOI: 10.3390/toxics12010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint. We then increased the complexity of encoders, incorporating graph-based methods, auto-encoders, and character embeddings employed in neural language processing. We also developed a multimodal neural network architecture, capable of considering the complementary nature of different chemical representations simultaneously. The potential of this approach, compared to more conventional architectures designed to handle a single encoder, becomes apparent when the amount of data increases.
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Affiliation(s)
- Edoardo Luca Viganò
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milan, Italy;
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Alessandra Roncaglioni
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milan, Italy;
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44
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Chunarkar-Patil P, Kaleem M, Mishra R, Ray S, Ahmad A, Verma D, Bhayye S, Dubey R, Singh HN, Kumar S. Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies. Biomedicines 2024; 12:201. [PMID: 38255306 PMCID: PMC10813144 DOI: 10.3390/biomedicines12010201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/01/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.
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Affiliation(s)
- Pritee Chunarkar-Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Mohammed Kaleem
- Department of Pharmacology, Dadasaheb Balpande, College of Pharmacy, Nagpur 440037, Maharashtra, India;
| | - Richa Mishra
- Department of Computer Engineering, Parul University, Ta. Waghodia, Vadodara 391760, Gujarat, India;
| | - Subhasree Ray
- Department of Life Science, Sharda School of Basic Sciences and Research, Greater Noida 201310, Uttar Pradesh, India
| | - Aftab Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Pharmacovigilance and Medication Safety Unit, Center of Research Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun 248002, Uttarkhand, India;
| | - Sagar Bhayye
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Himanshu Narayan Singh
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sanjay Kumar
- Biological and Bio-Computational Lab, Department of Life Science, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida 201310, Uttar Pradesh, India
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45
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Wieczorkiewicz PA, Shahamirian M, Kupka T, Makieieva N, Krygowski TM, Szatylowicz H. Unraveling the Push-Pull Effect in Acenes, Polyenes and Polyynes. Chemistry 2024; 30:e202303207. [PMID: 37955341 DOI: 10.1002/chem.202303207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/14/2023]
Abstract
Substituent effects (SEs) are fundamental for predicting molecular reactivity, while polyene, polyyne and acene derivatives are precursors to compounds with diverse applications. Computations were performed for Y-R-X systems, where reaction sites Y=NO2 and O- , substituents X=NO2 , CN, Cl, H, OH, NH2 , and spacers R=polyene, polyyne (n=1-5, 10 repeating units) and acene (up to tetracene). The cSAR (charge of the substituent active region) approach allowed to present, for the first time, quantitative relations describing the spacer's electron-donating and withdrawing properties as a function of n and the spacer type. The electronic properties of the X substituents depend on the type of spacer, its length and the Y group, which is an example of the reverse SE. To describe how the SE between Y and X weakens with n, two approaches were compared: cSAR and SESE (SE stabilization energy). The EDDB (electron density of delocalized bonds) characterize changes in electron delocalization in spacers due to the SE. A new approach - EDDB differential maps - allow to extract the effect of X substitution on the electron delocalization. The charges at spacer's C atoms correlate with cSAR; changes in the slopes confirm the charge transfer by resonance.
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Affiliation(s)
- Paweł A Wieczorkiewicz
- Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664, Warsaw, Poland
| | - Mozhgan Shahamirian
- Department of Chemistry, Faculty of Science, Sarvestan Branch, Islamic Azad University, 73451-173, Sarvestan, Iran
| | - Teobald Kupka
- Faculty of Chemistry, University of Opole, Oleska 48, 45-052, Opole, Poland
| | - Natalina Makieieva
- Faculty of Chemistry, University of Opole, Oleska 48, 45-052, Opole, Poland
| | - Tadeusz M Krygowski
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Halina Szatylowicz
- Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664, Warsaw, Poland
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46
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Siddiqui NF, Vishwakarma P, Thakur S, Jadhav HR. Bioactivity predictions and virtual screening using machine learning predictive model. J Biomol Struct Dyn 2024:1-20. [PMID: 38217308 DOI: 10.1080/07391102.2023.2300132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Noor Fatima Siddiqui
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Pinky Vishwakarma
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Shikha Thakur
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Hemant R Jadhav
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
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47
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Gao N, Yang Y, Wang Z, Guo X, Jiang S, Li J, Hu Y, Liu Z, Xu C. Viscosity of Ionic Liquids: Theories and Models. Chem Rev 2024; 124:27-123. [PMID: 38156796 DOI: 10.1021/acs.chemrev.3c00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.
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Affiliation(s)
- Na Gao
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Ye Yang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Zhiyuan Wang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Xin Guo
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Siqi Jiang
- Sinopec Engineering Incorporation, Beijing 100195, P. R. China
| | - Jisheng Li
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Yufeng Hu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing at Karamay, Karamay 834000, China
| | - Zhichang Liu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Chunming Xu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
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48
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Tang W, Zhang X, Hong H, Chen J, Zhao Q, Wu F. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:155. [PMID: 38251120 PMCID: PMC10819018 DOI: 10.3390/nano14020155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/08/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.
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Affiliation(s)
- Weihao Tang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
| | - Xuejiao Zhang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Qing Zhao
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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49
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Kramer L, Schulze T, Klüver N, Altenburger R, Hackermüller J, Krauss M, Busch W. Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment. Sci Data 2024; 11:60. [PMID: 38200014 PMCID: PMC10781676 DOI: 10.1038/s41597-023-02904-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.
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Affiliation(s)
- Lena Kramer
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany
| | - Tobias Schulze
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany.
| | - Nils Klüver
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany
| | - Rolf Altenburger
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany
- RWTH Aachen University, Institute for Environmental Research, 52074, Aachen, Germany
| | - Jörg Hackermüller
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany
- University of Leipzig, Faculty of Mathematics and Computer Science, Ritterstr. 26, 04109, Leipzig, Germany
| | - Martin Krauss
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany
| | - Wibke Busch
- Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany.
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50
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Vuppala S, Chitumalla RK, Choi S, Kim T, Park H, Jang J. Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine. ACS OMEGA 2024; 9:994-1000. [PMID: 38222596 PMCID: PMC10785072 DOI: 10.1021/acsomega.3c07208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 01/16/2024]
Abstract
Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model.
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Affiliation(s)
- Srimai Vuppala
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Ramesh Kumar Chitumalla
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Seyong Choi
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Taeho Kim
- Department
of Bioscience and Biotechnology, Sejong
University, Seoul 05006, Republic
of Korea
| | - Hwangseo Park
- Department
of Bioscience and Biotechnology, Sejong
University, Seoul 05006, Republic
of Korea
| | - Joonkyung Jang
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
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