1
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Rath M, Wellnitz J, Martin HJ, Melo-Filho C, Hochuli JE, Silva GM, Beasley JM, Travis M, Sessions ZL, Popov KI, Zakharov AV, Cherkasov A, Alves V, Muratov EN, Tropsha A. Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles. J Med Chem 2024; 67:6508-6518. [PMID: 38568752 DOI: 10.1021/acs.jmedchem.3c02446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.
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Affiliation(s)
- Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Cleber Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Joshua E Hochuli
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guilherme Martins Silva
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jon-Michael Beasley
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maxfield Travis
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L Sessions
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Konstantin I Popov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada
| | - Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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2
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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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3
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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4
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Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? Int J Mol Sci 2021; 22:ijms22105212. [PMID: 34069090 PMCID: PMC8156896 DOI: 10.3390/ijms22105212] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023] Open
Abstract
A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.
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5
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Borba JVB, Braga RC, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Andrade CH. Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers. Chem Res Toxicol 2020; 34:258-267. [PMID: 32673477 DOI: 10.1021/acs.chemrestox.0c00186] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
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Affiliation(s)
- Joyce V B Borba
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.,Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | | | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.,Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, Paraíba 58059, Brazil
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil
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6
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Fourches D, Ash J. 4D- quantitative structure-activity relationship modeling: making a comeback. Expert Opin Drug Discov 2019; 14:1227-1235. [PMID: 31513441 DOI: 10.1080/17460441.2019.1664467] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Jeremy Ash
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
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7
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Chauhan S, Kumar A. Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:277-294. [PMID: 29390919 DOI: 10.1080/1062936x.2018.1426626] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 01/08/2018] [Indexed: 06/07/2023]
Abstract
Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of Y-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (r2 = 0.830, [Formula: see text] = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.
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Affiliation(s)
- S Chauhan
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , India
| | - A Kumar
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , India
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8
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Dreher J, Scheiber J, Stiefl N, Baumann K. xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles. J Chem Inf Model 2018; 58:165-181. [PMID: 29172519 DOI: 10.1021/acs.jcim.7b00419] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.
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Affiliation(s)
- Jan Dreher
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Josef Scheiber
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Nikolaus Stiefl
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
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Abstract
In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.
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Affiliation(s)
- Adeel Malik
- Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India
| | - Hemajit Singh
- Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India
| | - Munazah Andrabi
- Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India
| | - Syed Akhtar Husain
- Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India
| | - Shandar Ahmad
- Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India
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10
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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11
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1053] [Impact Index Per Article: 105.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A. Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model 2012; 52:2570-8. [PMID: 23030316 DOI: 10.1021/ci300338w] [Citation(s) in RCA: 161] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.
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Affiliation(s)
- Todd M Martin
- Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, USA.
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol Inform 2012; 31:202-21. [PMID: 27477092 DOI: 10.1002/minf.201100129] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Accepted: 02/04/2012] [Indexed: 11/10/2022]
Abstract
This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.
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Affiliation(s)
- Eugene N Muratov
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394. , .,Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman School of Pharmacy, University of North Carolina, Beard Hall 301, CB#7568, Chapel Hill, NC, 27599, USA tel: +19199663459, fax: +19199660204. ,
| | - Ekaterina V Varlamova
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Pavel G Polishchuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
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QSAR analysis of [(biphenyloxy)propyl]isoxazoles: agents against coxsackievirus B3. Future Med Chem 2011; 3:15-27. [PMID: 21428823 DOI: 10.4155/fmc.10.278] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Antiviral drugs are urgently needed for the treatment of acute and chronic diseases caused by enteroviruses such as coxsackievirus B3 (CVB3). The main goal of this study is quantitative structure-activity relationship (QSAR) analysis of anti-CVB3 activity (clinical CVB3 isolate 97927 [log IC50, µM]) and investigation of the selectivity of 25 ([biphenyloxy]propyl)isoxazoles, followed by computer-aided design and virtual screening of novel active compounds. DISCUSSION The 2D QSAR obtained models are quite satisfactory (R(2) = 0.84-0.99, Q(2) = 0.76-0.92, R(2)(ext) = 0.62-0.79). Compounds with high antiviral activity and selectivity have to contain 5-trifluoromethyl-[1,2,4]oxadiazole or 2,4-difluorophenyl fragments. Insertion of 2,5-dimethylbenzene, napthyl and especially biphenyl substituents into investigated compounds substantially decreases both their antiviral activity and selectivity. Several compounds were proposed as a result of design and virtual screening. A high level of activity of 2-methoxy-1-phenyl-1H-imidazo[4,5-c]pyridine (sm428) was confirmed experimentally. CONCLUSION Simplex representation of molecular structure allows successful QSAR analysis of anti-CVB3 activity of ([biphenyloxy]propyl)isoxazole derivatives. Two possible ways of battling CVB3 are considered as a future perspective.
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Artemenko A, Muratov EN, Kuz’min V, Muratov N, Varlamova E, Kuz'mina A, Gorb LG, Golius A, Hill F, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2011; 22:575-601. [PMID: 21714735 PMCID: PMC3442116 DOI: 10.1080/1062936x.2011.569950] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).
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Affiliation(s)
- A.G. Artemenko
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - E. N. Muratov
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - V.E. Kuz’min
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - N.N. Muratov
- Odessa National Polytechnic University, 1 Shevchenko Ave., Odessa, 65000, Ukraine
| | - E.V. Varlamova
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - A.V. Kuz'mina
- Odessa National Medicinal University, 2 Ol'gievskaya Str, Odessa, 65000, Ukraine
| | - L. G. Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - A. Golius
- Kharkiv National V.N. Karazin University, Department of Radophysics, Karkiv, 61077, Ukraine
| | - F.C. Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - J. Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - A. Tropsha
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
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Per aspera ad astra: application of Simplex QSAR approach in antiviral research. Future Med Chem 2010; 2:1205-26. [DOI: 10.4155/fmc.10.194] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
This review explores the application of the Simplex representation of molecular structure (SiRMS) QSAR approach in antiviral research. We provide an introduction to and description of SiRMS, its application in antiviral research and future directions of development of the Simplex approach and the whole QSAR field. In the Simplex approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The main advantages of SiRMS are consideration of the different physical–chemical properties of atoms, high adequacy and good interpretability of models obtained and clear procedures for molecular design. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic and biological experiments. The SiRMS approach is realized as the complex of the computer program ‘HiT QSAR’, which is available on request.
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Romanovskaya I, Kuz’min V, Oseychuk O, Muratov E, Artemenko A, Andronati S. QSPR Analysis of Peroxidase Substrates Reactivity. CHEMISTRY & CHEMICAL TECHNOLOGY 2009. [DOI: 10.23939/chcht03.04.255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Quantitative structure-property relationship (QSPR) analysis of phenol derivatives reactivity in the horseradish peroxidase catalyzed oxidative reactions was carried out. The statistic models, which describe the substituted phenols reactivity (Кm-1, Vmax) quite adequately, were obtained by multiple linear regression and partial least squares (PLS) methods. The electronic parameters of molecules, their lipophylicity, molecular refraction, and form parameters were used as descriptors for molecular structure. The obtained models allow to predict the reactivity of the new phenolic substrates with satisfactory reliability.
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Puzyn T, Leszczynski J, Cronin MT. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology. RECENT ADVANCES IN QSAR STUDIES 2009; 8. [PMCID: PMC7120998 DOI: 10.1007/978-1-4020-9783-6_5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it’s a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the “molecular alignment” problem, consideration of different physical–chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D–4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the “HiT QSAR” software that so includes powerful statistical capabilities and a number of useful utilities.
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Affiliation(s)
- Tomasz Puzyn
- Dept. Chemistry, University of Gdansk, ul. Jana Sobieskiego 18, Gdansk, 80-952 Poland
| | - Jerzy Leszczynski
- Dept. Chemistry, Jackson State University, J. R. Lynch St. 1325, Jackson, 39217 U.S.A
| | - Mark T. Cronin
- Dept. Chemistry, John Moores University, Byrom St., Liverpool, L3 3AF United Kingdom
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Kuz'min V, Muratov E, Artemenko A, Varlamova E, Gorb L, Wang J, Leszczynski J. Consensus QSAR Modeling of Phosphor-Containing Chiral AChE Inhibitors. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860117] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Koroleva LS, Kuz'min VE, Muratov EN, Artemenko AG, Sil'nikov VN. [Artificial ribonucleases: quantitative analysis of the structure-activity relationship and new insight into the strategy of design of highly efficient RNase mimetics]. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2008; 34:495-505. [PMID: 18695722 DOI: 10.1134/s1068162008040080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The dependence of hydrolytic activity of artificial ribonucleases toward an HIV-I RNA fragment, a 21-mer oligonucleotide, and tRNA Asp on the structure of the RNase mimetic was analyzed. The quantitative structure-activity relationship (QSAR task) was determined by the method of simplex representation of the molecular structure where the amounts of four-atom fragments (simplexes) of fixed structure, symmetry, and chirality served as descriptors. Not only the types of atoms participating in simplexes but also their physicochemical properties (e.g., partial charges, lipophilicities, etc.) were taken into account. This allowed the estimation of the relative role of various factors affecting the interaction of molecules under study with the corresponding biological target. The 2D QSAR models obtained by the method of projection to latent structures have quite satisfactory statistical indices (R2 = 0.82-0.96; Q2 = 0.73-0.89), which help predict the activities of new compounds. The electrostatic properties of ribonuclease atoms were shown to contribute significantly to the manifestation of the hydrolytic activity of ribonucleases in the case of the 21-mer oligonucleotide and tRNA. In addition, the structural fragments that most greatly contribute to the alteration of the hydrolytic activity of RNases were identified. The models obtained were used for the virtual screening and molecular design of new highly efficient RNase mimetics.
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Kuz'min VE, Muratov EN, Artemenko AG, Gorb L, Qasim M, Leszczynski J. The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study. J Comput Aided Mol Des 2008; 22:747-59. [PMID: 18385948 DOI: 10.1007/s10822-008-9211-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2007] [Accepted: 03/18/2008] [Indexed: 11/30/2022]
Abstract
The present study applies the Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory (R2 = 0.96-0.98; Q2 = 0.91-0.93; R2 (test) = 0.89-0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C-H fragments of the aromatic ring.
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Affiliation(s)
- Victor E Kuz'min
- Department of Chemistry, Computational Center for Molecular Structure and Interactions, Jackson State University, 1400 J.R. Lynch St, Jackson, MS 39217, USA
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Hierarchical QSAR technology based on the Simplex representation of molecular structure. J Comput Aided Mol Des 2008; 22:403-21. [DOI: 10.1007/s10822-008-9179-6] [Citation(s) in RCA: 98] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2007] [Accepted: 01/10/2008] [Indexed: 10/22/2022]
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Kuz'min VE, Artemenko AG, Muratov EN, Volineckaya IL, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. Quantitative structure-activity relationship studies of [(biphenyloxy)propyl]isoxazole derivatives. Inhibitors of human rhinovirus 2 replication. J Med Chem 2007; 50:4205-13. [PMID: 17665898 DOI: 10.1021/jm0704806] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The 50% cytotoxic concentration (CC50) in HeLa cells, the 50% inhibitory concentration (IC50) against human rhinovirus 2 (HRV-2), and the selectivity index (SI = CC50/IC50) of [(biphenyloxy)propyl]isoxazole derivatives were used to develop quantitative structure-activity relationships (QSAR) based on simplex representation of molecular structure. Statistic characteristics for partial least-squares models are quite satisfactory (R2 = 0.838 - 0.918; Q2 = 0.695 - 0.87) for prediction of CC50, IC50, and SI values and permit the virtual screening and molecular design of new compounds with strong anti-HRV-2 activity. The quality of prognosis for designed compounds was additionally estimated by analysis of domain applicability for each QSAR model. A hypothesis to the effect that terminal benzene substituents must have negative electrostatic potential and definite length (approximately 5.5-5.6 A) to possess strong antiviral activity has been suggested. The quality of developed analysis, i.e., high level of antiviral action of three new designed compounds, has been confirmed experimentally.
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Affiliation(s)
- Victor E Kuz'min
- A.V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine, Research Center for Antibiotics, Moscow, Russia
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Artemenko AG, Muratov EN, Kuz'min VE, Kovdienko NA, Hromov AI, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. Identification of individual structural fragments of N,N'-(bis-5-nitropyrimidyl)dispirotripiperazine derivatives for cytotoxicity and antiherpetic activity allows the prediction of new highly active compounds. J Antimicrob Chemother 2007; 60:68-77. [PMID: 17550890 DOI: 10.1093/jac/dkm172] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES The objectives of this study were (i) to apply computer-based technologies to evaluate the structure of 48 N,N'-(bis-5-nitropyrimidyl)dispirotripiperazines which belong to a new class of highly active antiviral compounds binding to cell surface heparan sulphates, (ii) to understand the chemical- biological interactions governing their activities, and (iii) to design new compounds with strong antiviral activity. METHODS The logarithm of 50% cytotoxic concentration (CC(50)) in GMK cells, of 50% inhibitory concentration (IC(50)) against herpes simplex virus type 1, and of selectivity index (SI = CC(50)/IC(50)) was used to develop quantitative structure-activity relationships (QSARs) based on simplex representation of molecular structure. The QSAR model was applied to design new compounds. Two of these compounds were synthesized, physico-chemically characterized and tested for cytotoxicity and antiviral activity. RESULTS Statistic characteristics for partial least squares models allow the prediction of CC(50), IC(50) and SI values. The QSAR results demonstrate a high impact of individual structural fragments for antiviral activity. Molecular fragments that promote and interfere with antiviral activity were defined on the basis of the obtained models. Electrostatic factors (38%) and hydrophobicity (34%) were the most important determinants of antiherpetic activity. Using the established method, new potential dispirotripiperazine derivatives were computationally designed. Two of these computationally designed compounds were synthesized. The biological test results confirm the computationally predicted values of these compounds. CONCLUSIONS The established QSAR model is suitable for the design of new antiherpetic compounds and prediction of their activity.
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Affiliation(s)
- A G Artemenko
- A.V. Bogatsky Physical-Chemical Institute, Lustdorfskaya doroga 86, Odessa, Ukraine
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Kuz'min VE, Artemenko AG, Polischuk PG, Muratov EN, Hromov AI, Liahovskiy AV, Andronati SA, Makan SY. Hierarchic system of QSAR models (1D–4D) on the base of simplex representation of molecular structure. J Mol Model 2005; 11:457-67. [PMID: 16237516 DOI: 10.1007/s00894-005-0237-x] [Citation(s) in RCA: 75] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2004] [Accepted: 12/08/2004] [Indexed: 11/29/2022]
Abstract
In this work, a hierarchic system of QSAR models from 1D to 4D is considered on the basis of the simplex representation of molecular structure (SiRMS). The essence of this system is that the QSAR problem is solved sequentially in a series of the improved models of the description of molecular structure. Thus, at each subsequent stage of a hierarchic system, the QSAR problem is not solved ab ovo, but rather the information obtained from the previous step is used. Actually, we deal with a system of solutions defined more exactly. In the SiRMS approach, a molecule is represented as a system of different simplex descriptors (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex-descriptor detail increases consecutively from 1D to 4D representations of molecular structure. It enables us to determine the fragments of structure that promote or interfere with the given biological activity easily. Molecular design of compounds with a given level of activity is possible on the basis of SiRMS. The efficiency of the method is demonstrated for the example of the analysis of substituted piperazines affinity for the 5-HT1A receptor.
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Affiliation(s)
- Victor E Kuz'min
- A.V. Bogatsky Physico-Chemical Institute of the National Academy of Sciences, 7 Ukraine, Lustdorfskaya doroga 86, Odessa, 65080, Ukraine.
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