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Schuh MG, Boldini D, Sieber SA. Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery. J Chem Inf Model 2024. [PMID: 38836773 DOI: 10.1021/acs.jcim.4c00765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.
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Affiliation(s)
- Maximilian G Schuh
- TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Technical University of Munich, 85748 Garching bei München, Germany
| | - Davide Boldini
- TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Technical University of Munich, 85748 Garching bei München, Germany
| | - Stephan A Sieber
- TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Technical University of Munich, 85748 Garching bei München, Germany
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2
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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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3
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Yasir M, Park J, Chun W. Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation. ACS OMEGA 2024; 9:20338-20349. [PMID: 38737046 PMCID: PMC11079907 DOI: 10.1021/acsomega.4c00820] [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: 01/24/2024] [Revised: 04/15/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024]
Abstract
Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.
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Affiliation(s)
- Muhammad Yasir
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Jinyoung Park
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Wanjoo Chun
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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5
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Metcalf DP, Glick ZL, Bortolato A, Jiang A, Cheney DL, Sherrill CD. Directional Δ G Neural Network (DrΔ G-Net): A Modular Neural Network Approach to Binding Free Energy Prediction. J Chem Inf Model 2024; 64:1907-1918. [PMID: 38470995 DOI: 10.1021/acs.jcim.3c02054] [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: 03/14/2024]
Abstract
The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.
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Affiliation(s)
- Derek P Metcalf
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Zachary L Glick
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Andrea Bortolato
- Molecular Structure and Design, Bristol-Myers Squibb Company, P.O. Box 5400, Princeton, New Jersey 08543, United States
| | - Andy Jiang
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Daniel L Cheney
- Molecular Structure and Design, Bristol-Myers Squibb Company, P.O. Box 5400, Princeton, New Jersey 08543, United States
| | - C David Sherrill
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
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Sobh EA, Dahab MA, Elkaeed EB, Alsfouk AA, Ibrahim IM, Metwaly AM, Eissa IH. Computer aided drug discovery (CADD) of a thieno[2,3- d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket. J Biomol Struct Dyn 2024; 42:2369-2391. [PMID: 37129193 DOI: 10.1080/07391102.2023.2204500] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of -98.44 and -88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells' growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Eman A Sobh
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Menoufia University, Shibin-Elkom, Menoufia, Egypt
| | - Mohammed A Dahab
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Aisha A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ibrahim M Ibrahim
- Biophysics Department, Faculty of Science, Cairo University, Cairo, Egypt
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
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Boulaamane Y, Kandpal P, Chandra A, Britel MR, Maurady A. Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE. J Biomol Struct Dyn 2024; 42:1629-1646. [PMID: 37199265 DOI: 10.1080/07391102.2023.2209650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/05/2023] [Indexed: 05/19/2023]
Abstract
Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many natural products and is reported to display various pharmacological properties. A large plethora of compounds based on the coumarin ring system have been synthesized and were found to possess biological activities such as anticonvulsant, antiviral, anti-inflammatory, antibacterial, antioxidant as well as neuroprotective properties. Despite the wide activity spectrum of coumarins, its naturally occurring derivatives are yet to be investigated in detail. In the current study, a chemical library was created to assemble all chemical information related to naturally occurring coumarins from the literature. Additionally, a multi-stage virtual screening combining QSAR modeling, molecular docking, and ADMET prediction was conducted against monoamine oxidase B and acetylcholinesterase, two relevant targets known for their neuroprotective properties and 'disease-modifying' potential in Parkinson's and Alzheimer's disease. Our findings revealed ten coumarin derivatives that may act as dual-target drugs against MAO-B and AChE. Two coumarin candidates were selected from the molecular docking study: CDB0738 and CDB0046 displayed favorable interactions for both proteins as well as suitable ADMET profiles. The stability of the selected coumarins was assessed through 100 ns molecular dynamics simulations which revealed promising stability through key molecular interactions for CDB0738 to act as dual inhibitor of MAO-B and AChE. However, experimental studies are necessary to evaluate the bioactivity of the proposed candidate. The current results may generate an increasing interest in bioprospecting naturally occurring coumarins as potential candidates against relevant macromolecular targets by encouraging virtual screening studies against our chemical library.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yassir Boulaamane
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
| | | | | | - Mohammed Reda Britel
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Amal Maurady
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
- Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
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Jarrah A, Lababneh J. A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform. Curr Comput Aided Drug Des 2024; 20:236-247. [PMID: 37828771 DOI: 10.2174/1573409919666230503094411] [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: 09/29/2022] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking procedure. OBJECTIVE The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms separately. METHODS In this work, we have employed a new approach of hybridization between the Artificial Bee Colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. RESULTS The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 times. CONCLUSION Our novel approach was significantly effective to improve the functionality of docking algorithms (Bristol University Docking Engine (BUDE)).
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Affiliation(s)
- Amin Jarrah
- Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, 21163, Jordan
| | - Jawad Lababneh
- Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, 21163, Jordan
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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Chen J, Gu Z, Lai L, Pei J. In silico protein function prediction: the rise of machine learning-based approaches. MEDICAL REVIEW (2021) 2023; 3:487-510. [PMID: 38282798 PMCID: PMC10808870 DOI: 10.1515/mr-2023-0038] [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: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 01/30/2024]
Abstract
Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.
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Affiliation(s)
- Jiaxiao Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
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Yasir M, Park J, Han ET, Park WS, Han JH, Kwon YS, Lee HJ, Chun W. Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation. J Chem Inf Model 2023; 63:6487-6500. [PMID: 37906702 DOI: 10.1021/acs.jcim.3c01090] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.
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Affiliation(s)
- Muhammad Yasir
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Jinyoung Park
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Eun-Taek Han
- Department of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Won Sun Park
- Department of Physiology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Jin-Hee Han
- Department of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Yong-Soo Kwon
- College of Pharmacy, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Hee-Jae Lee
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Wanjoo Chun
- Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
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Eissa IH, Yousef RG, Elkady H, Elkaeed EB, Alsfouk AA, Husein DZ, Ibrahim IM, Radwan MM, Metwaly AM. A Theobromine Derivative with Anticancer Properties Targeting VEGFR-2: Semisynthesis, in silico and in vitro Studies. ChemistryOpen 2023; 12:e202300066. [PMID: 37803417 PMCID: PMC10558427 DOI: 10.1002/open.202300066] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/23/2023] [Indexed: 10/08/2023] Open
Abstract
A computer-assisted drug design (CADD) approach was utilized to design a new acetamido-N-(para-fluorophenyl)benzamide) derivative of the naturally occurring alkaloid, theobromine, (T-1-APFPB), following the pharmacophoric features of VEGFR-2 inhibitors. The stability and reactivity of T-1-AFPB were assessed through density functional theory (DFT) calculations. Molecular docking assessments showed T-1-AFPB's potential to bind with and inhibit VEGFR-2. The precise binding of T-1-AFPB against VEGFR-2 with optimal energy was further confirmed through several molecular dynamics (MD) simulations, PLIP, MM-GBSA, and PCA studies. Then, T-1-AFPB (4-(2-(3,7-Dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-1-yl)acetamido)-N-(4-fluorophenyl)benzamide) was semi-synthesized and the in vitro assays showed its potential to inhibit VEGFR-2 with an IC50 value of 69 nM (sorafenib's IC50 was 56 nM) and to inhibit the growth of HepG2 and MCF-7 cancer cell lines with IC50 values of 2.24±0.02 and 3.26±0.02 μM, respectively. Moreover, T-1-AFPB displayed very high selectivity indices against normal Vero cell lines. Furthermore, T-1-AFPB induced early (from 0.72 to 19.12) and late (from 0.13 to 6.37) apoptosis in HepG2 cell lines. In conclusion, the combined computational and experimental approaches demonstrated the efficacy and safety of T-1-APFPB providing it as a promising lead VEGFR-2 inhibitor for further development aiming at cancer therapy.
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Affiliation(s)
- Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design DepartmentFaculty of Pharmacy (Boys)Al-Azhar UniversityCairo11884Egypt
| | - Reda G. Yousef
- Pharmaceutical Medicinal Chemistry & Drug Design DepartmentFaculty of Pharmacy (Boys)Al-Azhar UniversityCairo11884Egypt
| | - Hazem Elkady
- Pharmaceutical Medicinal Chemistry & Drug Design DepartmentFaculty of Pharmacy (Boys)Al-Azhar UniversityCairo11884Egypt
| | - Eslam B. Elkaeed
- Department of Pharmaceutical SciencesCollege of PharmacyAlMaarefa UniversityRiyadh13713Saudi Arabia
| | - Aisha A. Alsfouk
- Department of Pharmaceutical SciencesCollege of PharmacyPrincess Nourah bint Abdulrahman UniversityP.O. Box 84428Riyadh11671Saudi Arabia
| | - Dalal Z. Husein
- Chemistry DepartmentFaculty of ScienceNew Valley UniversityEl-Kharja72511Egypt
| | | | - Mohamed M. Radwan
- National Center for Natural Products ResearchUniversity of MississippiMississippiMS 38677USA
- Department of PharmacognosyFaculty of PharmacyAlexandria UniversityAlexandriaEgypt
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants DepartmentFaculty of Pharmacy (Boys)Al-Azhar UniversityCairo11884Egypt
- Biopharmaceutical Products Research DepartmentGenetic Engineering and Biotechnology Research InstituteCity of Scientific Research and Technological Applications (SRTA-City)AlexandriaEgypt
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13
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Sabei FY, Y Safhi A, Almoshari Y, Salawi A, H Sultan M, Ali Bakkari M, Alsalhi A, A Madkhali O, M Jali A, Ahsan W. Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies. J Biomol Struct Dyn 2023:1-12. [PMID: 37776007 DOI: 10.1080/07391102.2023.2263588] [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: 04/03/2023] [Accepted: 08/28/2023] [Indexed: 10/01/2023]
Abstract
The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fahad Y Sabei
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Awaji Y Safhi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Yosif Almoshari
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Ahmad Salawi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Muhammad H Sultan
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Mohammed Ali Bakkari
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdullah Alsalhi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Osama A Madkhali
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdulmajeed M Jali
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Waquar Ahsan
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
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14
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Yasir M, Park J, Han ET, Park WS, Han JH, Kwon YS, Lee HJ, Chun W. Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis. ACS OMEGA 2023; 8:34160-34170. [PMID: 37744812 PMCID: PMC10515398 DOI: 10.1021/acsomega.3c05425] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023]
Abstract
Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.
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Affiliation(s)
- Muhammad Yasir
- Department
of Pharmacology, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
| | - Jinyoung Park
- Department
of Pharmacology, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
| | - Eun-Taek Han
- Department
of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Won Sun Park
- Department
of Physiology, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
| | - Jin-Hee Han
- Department
of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Yong-Soo Kwon
- College
of Pharmacy, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
| | - Hee-Jae Lee
- Department
of Pharmacology, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
| | - Wanjoo Chun
- Department
of Pharmacology, Kangwon National University
School of Medicine, Chuncheon24341, Republic
of Korea
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15
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Li L, Liu S, Wang B, Liu F, Xu S, Li P, Chen Y. An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches. Int J Mol Sci 2023; 24:13953. [PMID: 37762253 PMCID: PMC10530957 DOI: 10.3390/ijms241813953] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.
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Affiliation(s)
- Linwei Li
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Songtao Liu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
- Key Laboratory of Pesticide, College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China
| | - Bi Wang
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Fei Liu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Shu Xu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Pirui Li
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Yu Chen
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
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16
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Sobh EA, Dahab MA, Elkaeed EB, Alsfouk BA, Ibrahim IM, Metwaly AM, Eissa IH. A novel thieno[2,3-d]pyrimidine derivative inhibiting vascular endothelial growth factor receptor-2: A story of computer-aided drug discovery. Drug Dev Res 2023; 84:1247-1265. [PMID: 37232504 DOI: 10.1002/ddr.22083] [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: 01/22/2023] [Revised: 05/06/2023] [Accepted: 05/14/2023] [Indexed: 05/27/2023]
Abstract
Following the pharmacophoric features of vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors, a novel thieno[2,3-d]pyrimidine derivative has been designed and its activity against VEGFR-2 has been demonstrated by molecular docking studies that showed an accurate binding mode and an excellent binding energy. Furthermore, the recorded binding was confirmed by a series of molecular dynamics simulation studies, which also revealed precise energetic, conformational, and dynamic changes. Additionally, molecular mechanics with generalized Born and surface area solvation and polymer-induced liquid precursors studies were conducted and verified the results of the MD simulations. Next, in silico absorption, distribution, metabolism, excretion, and toxicity studies have also been conducted to examine the general drug-like nature of the designed candidate. According to the previous results, the thieno[2,3-d]pyrimidine derivative was synthesized. Fascinatingly, it inhibited VEGFR-2 (IC50 = 68.13 nM) and demonstrated strong inhibitory activity toward human liver (HepG2), and prostate (PC3) cell lines with IC50 values of 6.60 and 11.25 µM, respectively. As well, it was safe and showed a high selectivity index against normal cell lines (WI-38). Finally, the thieno[2,3-d]pyrimidine derivative arrested the growth of the HepG2 cells at the G2/M phase inducing both early and late apoptosis. These results were further confirmed through the ability of the thieno[2,3-d]pyrimidine derivative to induce significant changes in the apoptotic genes levels of caspase-3, caspase-9, Bcl-2 associated X-protein, and B-cell lymphoma 2.
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Affiliation(s)
- Eman A Sobh
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Menoufia University, Menoufia, Shibin-Elkom, Egypt
| | - Mohammed A Dahab
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Bshra A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ibrahim M Ibrahim
- Biophysics Department, Faculty of Science, Cairo University, Cairo, Egypt
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
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17
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Rashid PT, Hossain MJ, Zahan MS, Hasan CM, Rashid MA, Al-Mansur MA, Haque MR. Chemico-pharmacological and computational studies of Ophiorrhiza fasciculata D. Don and Psychotria silhetensis Hook. f. focusing cytotoxic, thrombolytic, anti-inflammatory, antioxidant, and antibacterial properties. Heliyon 2023; 9:e20100. [PMID: 37809757 PMCID: PMC10559867 DOI: 10.1016/j.heliyon.2023.e20100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 08/21/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
The current study sought to examine the pharmacological potentials of crude methanolic extracts of Ophiorrhiza fasciculata and Psychotria silhetensis, as well as their various solvent fractionates, with a focus on cytotoxic, thrombolytic, membrane stabilizing, antioxidant, and antibacterial activities via in vitro and in silico approaches. The extensive chromatographic and spectroscopic analyses confirmed and characterized two compounds as (±)-licarin B (1) and stigmasterol (2) from O. fasciculata and P. silhetensis, respectively. Petroleum ether soluble fraction of O. fasciculata and the aqueous soluble fraction of P. silhetensis showed the lowest 50% lethal concentrations (1.41 and 1.94 μg/mL, respectively) in brine shrimp bioassay. Likewise, petroleum ether soluble fraction of O. fasciculata and aqueous soluble fraction of P. silhetensis showed the highest thrombolytic activity with 46.66% and 50.10% lyses of the clot, respectively. The methanol and dichloromethane soluble fractions of O. fasciculata reduced erythrocyte hemolysis by 64.03% and 37.08%, respectively, under hypotonic and heat-induced conditions, compared to 81.97% and 42.12% for standard acetylsalicylic acid. In antioxidant activity test, aqueous soluble fraction O. fasciculata (IC50 = 7.22 μg/mL) revealed promising antioxidant potentialities in comparison to standard butylated hydroxytoluene (IC50 = 21.20 μg/mL). In antibacterial screening, chloroform, and dichloromethane soluble fractions of P. silhetensis showed a mild antibacterial activity compared with the standard drug ciprofloxacin. Additionally, the molecular docking study corroborated the current in vitro findings, and the isolated two constituents had higher binding affinities toward epidermal growth factor receptor, tissue plasminogen activator, vFLIP-IKK gamma stapled peptide dimer, glutathione reductase, and dihydrofolate reductase enzyme than their corresponding standard drugs. In addition, the both isolated compounds exerted favorable pharmacokinetics (absorption, distribution, metabolism, excretion) and toxicological profiles with drug-like qualities in computational-based ADMET and drug likeliness analyses. The current research suggests that both plants have potential as a natural treatment for treating thrombosis, inflammation, and oxidative stress. However, more thorough research is required to thoroughly screen for phytochemicals and pinpoint the precise mechanisms of action of the bioactive metabolites derived from these plants against a broad range of molecular targets.
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Affiliation(s)
- Parisa Tamannur Rashid
- Phytochemical Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
- Department of Pharmacy, East West University, Dhaka, Bangladesh
| | - Md Jamal Hossain
- Phytochemical Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
- Department of Pharmacy, State University of Bangladesh, 77 Satmasjid Road, Dhanmondi, Dhaka 1205, Bangladesh
| | - Miss Sharmin Zahan
- Department of Pharmacy, State University of Bangladesh, 77 Satmasjid Road, Dhanmondi, Dhaka 1205, Bangladesh
| | - Choudhury Mahmood Hasan
- Phytochemical Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
| | - Mohammad A. Rashid
- Phytochemical Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
| | - Muhammad Abdullah Al-Mansur
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dr. Qudrat-I-Khuda Road, Dhanmondi, Dhaka-1205, Bangladesh
| | - Mohammad Rashedul Haque
- Phytochemical Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
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18
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Yi J, Lee S, Lim S, Cho C, Piao Y, Yeo M, Kim D, Kim S, Lee S. Exploring chemical space for lead identification by propagating on chemical similarity network. Comput Struct Biotechnol J 2023; 21:4187-4195. [PMID: 37680266 PMCID: PMC10480321 DOI: 10.1016/j.csbj.2023.08.016] [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: 04/04/2023] [Revised: 08/08/2023] [Accepted: 08/20/2023] [Indexed: 09/09/2023] Open
Abstract
Motivation Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.
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Affiliation(s)
- Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sangsoo Lim
- School of AI Software Convergence, Dongguk University, Pildong-ro 1-gil, Jung-gu, Seoul, South Korea
| | - Changyun Cho
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Marie Yeo
- PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea
| | - Dongkyu Kim
- PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea
| | - Sun Kim
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sunho Lee
- AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
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19
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Miao Y, Ma H, Huang J. Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning. Chem Res Toxicol 2023; 36:1206-1226. [PMID: 37562046 DOI: 10.1021/acs.chemrestox.2c00384] [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: 08/12/2023]
Abstract
The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.
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Affiliation(s)
- Yuwei Miao
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States
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20
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Khan A, Sohail S, Yaseen S, Fatima S, Wisal A, Ahmed S, Nasir M, Irfan M, Karim A, Basharat Z, Khan Y, Aurongzeb M, Raza SK, Alshahrani MY, Morel CM, Hassan SS. Exploring and targeting potential druggable antimicrobial resistance targets ArgS, SecY, and MurA in Staphylococcus sciuri with TCM inhibitors through a subtractive genomics strategy. Funct Integr Genomics 2023; 23:254. [PMID: 37495774 DOI: 10.1007/s10142-023-01179-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/28/2023]
Abstract
Staphylococcus sciuri (also currently Mammaliicoccus sciuri) are anaerobic facultative and non-motile bacteria that cause significant human pathogenesis such as endocarditis, wound infections, peritonitis, UTI, and septic shock. Methicillin-resistant S. sciuri (MRSS) strains also infects animals that include healthy broilers, cattle, dogs, and pigs. The emergence of MRSS strains thereby poses a serious health threat and thrives the scientific community towards novel treatment options. Herein, we investigated the druggable genome of S. sciuri by employing subtractive genomics that resulted in seven genes/proteins where only three of them were predicted as final targets. Further mining the literature showed that the ArgS (WP_058610923), SecY (WP_058611897), and MurA (WP_058612677) are involved in the multi-drug resistance phenomenon. After constructing and verifying the 3D protein homology models, a screening process was carried out using a library of Traditional Chinese Medicine compounds (consisting of 36,043 compounds). The molecular docking and simulation studies revealed the physicochemical stability parameters of the docked TCM inhibitors in the druggable cavities of each protein target by identifying their druggability potential and maximum hydrogen bonding interactions. The simulated receptor-ligand complexes showed the conformational changes and stability index of the secondary structure elements. The root mean square deviation (RMSD) graph showed fluctuations due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points where the pattern of the RMSD and root mean square fluctuation (RMSF) (< 1.0 Å) support any major domain shifts within the structural framework of the protein-ligand complex and placement of ligand was well complemented within the binding site. The β-factor values demonstrated instability at few points while the radius of gyration for structural compactness as a time function for the 100-ns simulation of protein-ligand complexes showed favorable average values and denoted the stability of all complexes. It is assumed that such findings might facilitate researchers to robustly discover and develop effective therapeutics against S. sciuri alongside other enteric infections.
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Affiliation(s)
- Aafareen Khan
- Department of Chemistry, Islamia College Peshawar, Peshawar, 25000, KP, Pakistan
| | - Saman Sohail
- Department of Chemistry, Islamia College Peshawar, Peshawar, 25000, KP, Pakistan
| | - Seerat Yaseen
- Abbasi Shaheed Hospital, Karachi Medical and Dental College, Karachi, Pakistan
| | - Sareen Fatima
- Department of Microbiology, University of Balochistan, Quetta, Balochistan, Pakistan
| | - Ayesha Wisal
- Department of Chemistry, Islamia College Peshawar, Peshawar, 25000, KP, Pakistan
| | - Sufyan Ahmed
- Abbasi Shaheed Hospital, Karachi Medical and Dental College, Karachi, Pakistan
| | - Mahrukh Nasir
- Dr. Panjwani Center for Molecular Medicine, International Center for Chemical and Biological Sciences (ICCBS-PCMD), University of Karachi, Karachi, 75270, Pakistan
| | - Muhammad Irfan
- Dr. Panjwani Center for Molecular Medicine, International Center for Chemical and Biological Sciences (ICCBS-PCMD), University of Karachi, Karachi, 75270, Pakistan
| | - Asad Karim
- Dr. Panjwani Center for Molecular Medicine, International Center for Chemical and Biological Sciences (ICCBS-PCMD), University of Karachi, Karachi, 75270, Pakistan
| | - Zarrin Basharat
- Alpha Genomics (Private) Limited, Islamabad, 44710, Pakistan
| | - Yasmin Khan
- Dr. Panjwani Center for Molecular Medicine, International Center for Chemical and Biological Sciences (ICCBS-PCMD), University of Karachi, Karachi, 75270, Pakistan
| | - Muhammad Aurongzeb
- Faculty of Engineering Sciences & Technology, Hamdard University, Karachi, 74600, Pakistan
| | - Syed Kashif Raza
- Faculty of Rehabilitation and Allied Health Sciences (FRAHS), Riphah International University, Faisalabad, Pakistan
| | - Mohammad Y Alshahrani
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, P.O. Box 61413, Abha, 9088, Saudi Arabia
| | - Carlos M Morel
- Centre for Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Building "Expansão", 8Th Floor Room 814, Av. Brasil 4036 - Manguinhos, Rio de Janeiro, RJ, 21040-361, Brazil.
| | - Syed S Hassan
- Dr. Panjwani Center for Molecular Medicine, International Center for Chemical and Biological Sciences (ICCBS-PCMD), University of Karachi, Karachi, 75270, Pakistan.
- Centre for Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Building "Expansão", 8Th Floor Room 814, Av. Brasil 4036 - Manguinhos, Rio de Janeiro, RJ, 21040-361, Brazil.
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Damavandi S, Shiri F, Emamjomeh A, Pirhadi S, Beyzaei H. A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling. BMC Chem 2023; 17:70. [PMID: 37415191 DOI: 10.1186/s13065-023-00991-6] [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: 02/25/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023] Open
Abstract
Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.
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Affiliation(s)
- Sedigheh Damavandi
- Department of Bioinformatics, Laboratory of Computational Biotechnology and Bioinformatics (CBB Lab), University of Zabol, Zabol, Iran
| | - Fereshteh Shiri
- Department of Chemistry, Faculty of Science, University of Zabol, Zabol, Iran.
| | - Abbasali Emamjomeh
- Department of Bioinformatics, Laboratory of Computational Biotechnology and Bioinformatics (CBB Lab), University of Zabol, Zabol, Iran
- Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Beyzaei
- Department of Chemistry, Faculty of Science, University of Zabol, Zabol, Iran
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Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling. J Cheminform 2023; 15:60. [PMID: 37296454 DOI: 10.1186/s13321-023-00728-6] [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: 09/16/2022] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R2 determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .
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Affiliation(s)
- Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Anna Fava
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Vincenzo Pisapia
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Francesco Sacco
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Daniela Iaconis
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
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Bernal FA, Schmidt TJ. A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †. Molecules 2023; 28:molecules28083399. [PMID: 37110632 PMCID: PMC10144340 DOI: 10.3390/molecules28083399] [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: 03/08/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.
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Affiliation(s)
- Freddy A Bernal
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
| | - Thomas J Schmidt
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
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Ajala A, Uzairu A, Shallangwa GA, Abechi SE. Virtual screening, molecular docking simulation and ADMET prediction of some selected natural products as potential inhibitors of NLRP3 inflammasomes as drug candidates for Alzheimer disease. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2023. [DOI: 10.1016/j.bcab.2023.102615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Deschamps E, Calabrese V, Schmitz I, Hubert-Roux M, Castagnos D, Afonso C. Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis. Molecules 2023; 28:molecules28052061. [PMID: 36903305 PMCID: PMC10003995 DOI: 10.3390/molecules28052061] [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: 01/16/2023] [Revised: 02/16/2023] [Accepted: 02/19/2023] [Indexed: 02/25/2023] Open
Abstract
Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.
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Affiliation(s)
- Estelle Deschamps
- Normandie Univ, COBRA, UMR 6014 and FR 3038, Université de Rouen, INSA de Rouen, CNRS, IRCOF, 1 rue Tesnières, CEDEX, 76821 Mont-Saint-Aignan, France
- ORIL Industrie, Servier Group, 13 r Auguste Desgenétais, 76210 Bolbec, France
| | - Valentina Calabrese
- Normandie Univ, COBRA, UMR 6014 and FR 3038, Université de Rouen, INSA de Rouen, CNRS, IRCOF, 1 rue Tesnières, CEDEX, 76821 Mont-Saint-Aignan, France
- Université de Lyon, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, CNRS UMR 5280, 5 Rue de La Doua, F-69100 Villeurbanne, France
| | - Isabelle Schmitz
- Normandie Univ, COBRA, UMR 6014 and FR 3038, Université de Rouen, INSA de Rouen, CNRS, IRCOF, 1 rue Tesnières, CEDEX, 76821 Mont-Saint-Aignan, France
| | - Marie Hubert-Roux
- Normandie Univ, COBRA, UMR 6014 and FR 3038, Université de Rouen, INSA de Rouen, CNRS, IRCOF, 1 rue Tesnières, CEDEX, 76821 Mont-Saint-Aignan, France
| | - Denis Castagnos
- ORIL Industrie, Servier Group, 13 r Auguste Desgenétais, 76210 Bolbec, France
| | - Carlos Afonso
- Normandie Univ, COBRA, UMR 6014 and FR 3038, Université de Rouen, INSA de Rouen, CNRS, IRCOF, 1 rue Tesnières, CEDEX, 76821 Mont-Saint-Aignan, France
- Correspondence:
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Fereshteh S, Noori Goodarzi N, Kalhor H, Rahimi H, Barzi SM, Badmasti F. Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target. Bioinform Biol Insights 2023; 17:11779322231152980. [PMID: 36798081 PMCID: PMC9926382 DOI: 10.1177/11779322231152980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/24/2022] [Indexed: 02/17/2023] Open
Abstract
Background Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics. Methods In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated. Results A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine. Conclusion The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.
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Affiliation(s)
| | - Narjes Noori Goodarzi
- Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Hourieh Kalhor
- Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, Iran
| | - Hamzeh Rahimi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Farzad Badmasti
- Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Farzad Badmasti, Department of Bacteriology, Pasteur Institute of Iran, Tehran Province, Tehran, 12 Farvardin St, Tehran 1316943551, Iran.
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Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. CARBON 2023; 204:484-494. [PMID: 36845527 PMCID: PMC9957041 DOI: 10.1016/j.carbon.2022.12.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.
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Affiliation(s)
- Tong Wang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Daniel P. Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
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28
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Louis H, Chima CM, Amodu IO, Gber TE, Unimuke TO, Adeyinka AS. Organochlorine detection on transition metals (X=Zn, Ti, Ni, Fe, and Cr) anchored fullerenes (C
23
X). ChemistrySelect 2023. [DOI: 10.1002/slct.202203843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Hitler Louis
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
- Department of Pure and Applied Chemistry Faculty of Physical Sciences University of Calabar Calabar Nigeria
| | - Chioma M. Chima
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
- Department of Pure and Applied Chemistry Faculty of Physical Sciences University of Calabar Calabar Nigeria
| | - Ismail O. Amodu
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
- Department of Mathematics Faculty of Physical Sciences University of Calabar Calabar Nigeria
| | - Terkumbur E. Gber
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
- Department of Pure and Applied Chemistry Faculty of Physical Sciences University of Calabar Calabar Nigeria
| | - Tomsmith O. Unimuke
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
- Department of Pure and Applied Chemistry Faculty of Physical Sciences University of Calabar Calabar Nigeria
| | - Adedapo S. Adeyinka
- Department of Chemical Sciences University of Johannesburg Johannesburg South Africa
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A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study. LIFE (BASEL, SWITZERLAND) 2023; 13:life13010191. [PMID: 36676140 PMCID: PMC9867533 DOI: 10.3390/life13010191] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/25/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023]
Abstract
A new lead compound has been designed as an antiangiogenic EGFR inhibitor that has the pharmacophoric characteristics to bind with the catalytic pocket of EGFR protein. The designed lead compound is a (para-chloro)acetamide derivative of the alkaloid, theobromine, (T-1-PCPA). At first, we started with deep density functional theory (DFT) calculations for T-1-PCPA to confirm and optimize its 3D structure. Additionally, the DFT studies identified the electrostatic potential, global reactive indices and total density of states expecting a high level of reactivity for T-1-PCPA. Secondly, the affinity of T-1-PCPA to bind and inhibit the EGFR protein was studied and confirmed through detailed structure-based computational studies including the molecular docking against EGFRWT and EGFRT790M, Molecular dynamics (MD) over 100 ns, MM-GPSA and PLIP experiments. Before the preparation, the computational ADME and toxicity profiles of T-1-PCPA have been investigated and its safety and the general drug-likeness predicted. Accordingly, T-1-PCPA was semi-synthesized to scrutinize the proposed design and the obtained in silico results. Interestingly, T-1-PCPA inhibited in vitro EGFRWT with an IC50 value of 25.35 nM, comparing that of erlotinib (5.90 nM). Additionally, T-1-PCPA inhibited the growth of A549 and HCT-116 malignant cell lines with IC50 values of 31.74 and 20.40 µM, respectively, comparing erlotinib that expressed IC50 values of 6.73 and 16.35 µM, respectively.
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Vittorio S, Lunghini F, Pedretti A, Vistoli G, Beccari AR. Ensemble of structure and ligand-based classification models for hERG liability profiling. Front Pharmacol 2023; 14:1148670. [PMID: 37033661 PMCID: PMC10076575 DOI: 10.3389/fphar.2023.1148670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.
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Affiliation(s)
- Serena Vittorio
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | | | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | - Andrea R. Beccari
- EXSCALATE, Dompé Farmaceutici SpA, Napoli, Italy
- *Correspondence: Andrea R. Beccari,
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Liao Y, Cao P, Luo L. Identification of Novel Arachidonic Acid 15-Lipoxygenase Inhibitors Based on the Bayesian Classifier Model and Computer-Aided High-Throughput Virtual Screening. Pharmaceuticals (Basel) 2022; 15:1440. [PMID: 36422570 PMCID: PMC9695033 DOI: 10.3390/ph15111440] [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: 10/08/2022] [Revised: 11/11/2022] [Accepted: 11/16/2022] [Indexed: 08/29/2023] Open
Abstract
Ferroptosis is an iron-dependent lipid peroxidative form of cell death that is distinct from apoptosis and necrosis. ALOX15, also known as arachidonic acid 15-lipoxygenase, promotes ferroptosis by converting intracellular unsaturated lipids into oxidized lipid intermediates and is an important ferroptosis target. In this study, a naive Bayesian machine learning classifier with a structure-based, high-throughput screening approach and a molecular docking program were combined to screen for three compounds with excellent target-binding potential. In the absorption, distribution, metabolism, excretion, and toxicity characterization, three candidate molecules were predicted to exhibit drug-like properties. The subsequent molecular dynamics simulations confirmed their stable binding to the targets. The findings indicated that the compounds exhibited excellent potential ALOX15 inhibitor capacity, thereby providing novel candidates for the treatment of inflammatory ischemia-related diseases caused by ferroptosis.
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Affiliation(s)
- Yinglin Liao
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China
| | - Peng Cao
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
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32
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Mohammadi MD, Abbas F, Louis H, Afahanam LE, Gber TE. Intermolecular Interactions between Nitrosourea and Polyoxometalate compounds. ChemistrySelect 2022. [DOI: 10.1002/slct.202202535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
| | - Faheem Abbas
- Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Hitler Louis
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
| | - Lucy E. Afahanam
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
| | - Terkumbu E. Gber
- Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria
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33
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Tan H, Wu J, Zhang R, Zhang C, Li W, Chen Q, Zhang X, Yu H, Shi W. Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12391-12403. [PMID: 35960020 DOI: 10.1021/acs.est.2c02242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.
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Affiliation(s)
- Haoyue Tan
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Jinqiu Wu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Rong Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Chi Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Li
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Qinchang Chen
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
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34
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Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
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35
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Shan M, Jiang C, Qin L, Cheng G. A Review of Computational Methods in Predicting hERG Channel Blockers. ChemistrySelect 2022. [DOI: 10.1002/slct.202201221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Mengyi Shan
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
| | - Chen Jiang
- QuanMin RenZheng (HangZhou) Technology Co. Ltd. China
| | - Lu‐Ping Qin
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
| | - Gang Cheng
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
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36
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Rani I, Kalsi A, Kaur G, Sharma P, Gupta S, Gautam RK, Chopra H, Bibi S, Ahmad SU, Singh I, Dhawan M, Emran TB. Modern drug discovery applications for the identification of novel candidates for COVID-19 infections. Ann Med Surg (Lond) 2022; 80:104125. [PMID: 35845863 PMCID: PMC9273307 DOI: 10.1016/j.amsu.2022.104125] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/23/2022] Open
Abstract
In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection. Importance of advanced genome sequencing in analyzing the sequential change in DNA and RNA due to SARS-CoV-2 genome. Lead Identification studies with computer drug-aided design. Prevalence of some flavonoid glycosides in medicinal plants which have potential protective effects against COVID-19 infections. Docking studies with alkaloids, vanillin derivatives and their actions suggest possible SARS- CoV-2 inhibition. Modeling studies outcomes about ligand-based and structure based activity.
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37
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Yang R, Xia Y, Xian J, Yu H, Yan B, Cheng B. Identification of Potential Dual Farnesol X Receptor/Retinoid X Receptor α Agonists Based on Machine Learning Models, ADMET Prediction and Molecular Docking. ChemistrySelect 2022. [DOI: 10.1002/slct.202200715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ruo‐qi Yang
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250355 China
- Shandong University of Traditional Chinese Medicine Jinan 250355 China
| | - Yu Xia
- Shandong University of Traditional Chinese Medicine Jinan 250355 China
| | - Jin Xian
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250355 China
| | - Hui‐juan Yu
- Shandong University of Traditional Chinese Medicine Jinan 250355 China
| | - Bin Yan
- Shandong University of Traditional Chinese Medicine Jinan 250355 China
| | - Bin Cheng
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250355 China
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38
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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39
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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40
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Qi XW, Liu YM, Hu YK, Yuan H, Ayeni EA, Liao X. Ligand fishing based on tubular microchannel modified with monoamine oxidase B for screening of the enzyme's inhibitors from Crocus sativus and Edgeworthia gardneri. J Sep Sci 2022; 45:2394-2405. [PMID: 35461190 DOI: 10.1002/jssc.202200057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/24/2022] [Accepted: 04/17/2022] [Indexed: 11/07/2022]
Abstract
A novel strategy of performing ligand fishing with enzyme-modified open tubular microchannel was proposed for screening bioactive components present in medicinal plants. Monoamine oxidase B was immobilized onto the surface of the microchannel for the first time to specifically extract its ligands when the plant's extracts solution flows through the channel. The thermal and the storage stability of immobilized monoamine oxidase B were significantly enhanced after immobilization. Crocin I and Ⅱ were extracted from Crocus sativus, and tiliroside was extracted from Edgeworthia gardneri. All the three compounds were inhibitors of the enzyme with the half-maximal inhibitory concentration values of 26.70 ± 0.91, 19.88 ± 2.78, and 15.65 ± 0.85 μM, respectively. The enzyme inhibition kinetics and molecular docking were investigated. This is the first report on the inhibitory effects of tiliroside and crocin Ⅱ. The novel ligand fishing method proposed in this work possesses advantages of rapidness, high efficiency, and tiny sample consumption compared to routine ligand fishing, with promising potential for screening active natural products in complex mixtures.
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Affiliation(s)
- Xu-Wei Qi
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Yi-Ming Liu
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| | - Yi-Kao Hu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Hao Yuan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Emmanuel Ayodeji Ayeni
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Xun Liao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, P. R. China
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41
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Hasan MR, Alsaiari AA, Fakhurji BZ, Molla MHR, Asseri AH, Sumon MAA, Park MN, Ahammad F, Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules 2022; 27:4169. [PMID: 35807415 PMCID: PMC9268380 DOI: 10.3390/molecules27134169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 01/18/2023] Open
Abstract
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.
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Affiliation(s)
- Md Rifat Hasan
- Department of Mathematics, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Department of Applied Mathematics, Faculty of Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ahad Amer Alsaiari
- College of Applied Medical Science, Clinical Laboratories Science Department, Taif University, Taif 21944, Saudi Arabia;
| | - Burhan Zain Fakhurji
- iGene Medical Training and Molecular Research Center, Jeddah 21589, Saudi Arabia;
| | | | - Amer H. Asseri
- Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia
| | - Md Afsar Ahmed Sumon
- Department of Marine Biology, Faculty of Marine Sciences, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Moon Nyeo Park
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
| | - Foysal Ahammad
- Department of Biological Sciences, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Bonglee Kim
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
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42
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Ahmad K, Rizzi A, Capelli R, Mandelli D, Lyu W, Carloni P. Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective. Front Mol Biosci 2022; 9:899805. [PMID: 35755817 PMCID: PMC9216551 DOI: 10.3389/fmolb.2022.899805] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.
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Affiliation(s)
- Katya Ahmad
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
| | - Andrea Rizzi
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
- Atomistic Simulations, Istituto Italiano di Tecnologia, Genova, Italy
| | - Riccardo Capelli
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, Torino, Italy
| | - Davide Mandelli
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
| | - Wenping Lyu
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, China
| | - Paolo Carloni
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
- Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Jülich, Germany
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43
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Kandi V, Vundecode A, Godalwar TR, Dasari S, Vadakedath S, Godishala V. The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice. BORNEO JOURNAL OF PHARMACY 2022. [DOI: 10.33084/bjop.v5i2.3013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.
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44
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Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022; 13:864412. [PMID: 35592425 PMCID: PMC9110924 DOI: 10.3389/fphar.2022.864412] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/17/2022] Open
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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45
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Patil SP, Fattakhova E, Hofer J, Oravic M, Bender A, Brearey J, Parker D, Radnoff M, Smith Z. Machine-Learning Guided Discovery of Bioactive Inhibitors of PD1-PDL1 Interaction. Pharmaceuticals (Basel) 2022; 15:ph15050613. [PMID: 35631439 PMCID: PMC9145945 DOI: 10.3390/ph15050613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 02/06/2023] Open
Abstract
The selective activation of the innate immune system through blockade of immune checkpoint PD1-PDL1 interaction has proven effective against a variety of cancers. In contrast to six antibody therapies approved and several under clinical investigation, the development of small-molecule PD1-PDL1 inhibitors is still in its infancy with no such drugs approved yet. Nevertheless, a promising series of small molecules inducing PDL1 dimerization has revealed important spatio-chemical features required for effective PD1-PDL1 inhibition through PDL1 sequestration. In the present study, we utilized these features for developing machine-learning (ML) classifiers by fitting Random Forest models to six 2D fingerprint descriptors. A focused database of ~16 K bioactive molecules, including approved and experimental drugs, was screened using these ML models, leading to classification of 361 molecules as potentially active. These ML hits were subjected to molecular docking studies to further shortlist them based on their binding interactions within the PDL1 dimer pocket. The top 20 molecules with favorable interactions were experimentally tested using HTRF human PD1-PDL1 binding assays, leading to the identification of two active molecules, CRT5 and P053, with the IC50 values of 22.35 and 33.65 µM, respectively. Owing to their bioactive nature, our newly discovered molecules may prove suitable for further medicinal chemistry optimization, leading to more potent and selective PD1-PDL1 inhibitors. Finally, our ML models and the integrated screening protocol may prove useful for screening larger libraries for novel PD1-PDL1 inhibitors.
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Affiliation(s)
- Sachin P. Patil
- NanoBio Lab, School of Engineering, Widener University, Chester, PA 19013, USA
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
- Correspondence: ; Tel.: +1-610-499-4492
| | - Elena Fattakhova
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
| | - Jeremy Hofer
- Department of Computer Science, Widener University, Chester, PA 19013, USA;
| | - Michael Oravic
- Department of Biomedical Engineering, Widener University, Chester, PA 19013, USA;
| | - Autumn Bender
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
| | - Jason Brearey
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
| | - Daniel Parker
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
| | - Madison Radnoff
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
| | - Zackary Smith
- Department of Chemical Engineering, Widener University, Chester, PA 19013, USA; (E.F.); (A.B.); (J.B.); (D.P.); (M.R.); (Z.S.)
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López-López E, Fernández-de Gortari E, Medina-Franco JL. Yes SIR! On the structure-inactivity relationships in drug discovery. Drug Discov Today 2022; 27:2353-2362. [PMID: 35561964 DOI: 10.1016/j.drudis.2022.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/09/2022] [Accepted: 05/05/2022] [Indexed: 12/12/2022]
Abstract
In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.
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Affiliation(s)
- Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico.
| | - Eli Fernández-de Gortari
- Department of Nanosafety, International Iberian Nanotechnology Laboratory, Braga 4715-330, Portugal
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [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: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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48
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From traditional to data-driven medicinal chemistry: a case study. Drug Discov Today 2022; 27:2065-2070. [PMID: 35452790 DOI: 10.1016/j.drudis.2022.04.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 12/20/2022]
Abstract
Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and effort until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and until experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data-driven approaches into practical medicinal chemistry and quantify the impact, as reported herein. Although the organization and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate the significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists. Keywords: medicinal chemistry; drug discovery; chemoinformatics; data science; data-driven R&D.
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Zhang FM, Yuan L, Shi XW, Feng KR, Lan X, Huang C, Lin GQ, Tian P, Huang M, Tang S, Gao D. Discovery of PHGDH inhibitors by virtual screening and preliminary structure–activity relationship study. Bioorg Chem 2022; 121:105705. [DOI: 10.1016/j.bioorg.2022.105705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/02/2022]
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50
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Wang J, Bhattarai A, Do HN, Akhter S, Miao Y. Molecular Simulations and Drug Discovery of Adenosine Receptors. Molecules 2022; 27:2054. [PMID: 35408454 PMCID: PMC9000248 DOI: 10.3390/molecules27072054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 02/02/2023] Open
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.
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Affiliation(s)
| | | | | | | | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA; (J.W.); (A.B.); (H.N.D.); (S.A.)
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