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Panwar P, Yang Q, Martini A. Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations. J Chem Inf Model 2024; 64:2760-2774. [PMID: 37582234 DOI: 10.1021/acs.jcim.3c00231] [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: 08/17/2023]
Abstract
Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.
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Affiliation(s)
- Pawan Panwar
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, California 95343, United States
| | - Quanpeng Yang
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, California 95343, United States
| | - Ashlie Martini
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, California 95343, United States
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2
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Tullius Scotti M, Herrera-Acevedo C, Barros de Menezes RP, Martin HJ, Muratov EN, Ítalo de Souza Silva Á, Faustino Albuquerque E, Ferreira Calado L, Coy-Barrera E, Scotti L. MolPredictX: Online Biological Activity Predictions by Machine Learning Models. Mol Inform 2022; 41:e2200133. [PMID: 35961924 DOI: 10.1002/minf.202200133] [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: 06/06/2022] [Accepted: 08/12/2022] [Indexed: 01/05/2023]
Abstract
Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in-house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars-CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.
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Affiliation(s)
- Marcus Tullius Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Chonny Herrera-Acevedo
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil.,Department of Chemical Engineering, Universidad ECCI, Carrera 19 # 49-20, 111311, Bogotá D.C., Colombia
| | - Renata Priscila Barros de Menezes
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - 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, NC, 27599, USA
| | - 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, NC, 27599, USA
| | - Ávilla Ítalo de Souza Silva
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Emmanuella Faustino Albuquerque
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Lucas Ferreira Calado
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá, 250247, Colombia
| | - Luciana Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
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Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
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Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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4
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Hosseini S, Ketabi S, Hasheminasab G. QSAR study of antituberculosis activity of oxadiazole derivatives using DFT calculations. J Recept Signal Transduct Res 2022; 42:503-511. [PMID: 35263550 DOI: 10.1080/10799893.2022.2044860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Mycobacterium tuberculosis (Mtb) is the causative agent of infectious diseases worldwide. Oxadiazole derivatives have many biological activities and can be a good alternative to antimicrobial drugs. In this study, the quantitative structure-activity relationship (QSAR) of fifty-one novel oxadiazoles derivatives has been studied using the density functional theory (DFT) and statistical methods. Becke's three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6-31++G (d) basis set are used to calculated quantum chemical descriptors using Gaussian09 software. The other descriptors including Lipinski, physicochemistry, topological, etc. were calculated using Chembio3d software. Statistically, the best correlation between the independent variables and the PMIC as the dependent variable was a 6-variable equation for which the correlation coefficient were as follows R2 = 0.86 and R = 0.93. Also, the values of MAE = 0.003 and Q2CV = 0.9 confirm the acceptability of the obtained model. The obtained equation shows that NRB, energy gap (ΔE), Henry's law constant, O-C, and C-N bonds length, and the Free Gibbs energy have the highest correlation with the anti-Tb activity.
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Affiliation(s)
- Sharieh Hosseini
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Sepideh Ketabi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Golnar Hasheminasab
- School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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User-assisted methodology targeted for building structure interpretable QSPR models for boosting CO2 capture with ionic liquids. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Migliorati S, Monti GS, Vighi M. Ecological hazard assessment via species sensitivity distributions: The non-exchangeability issue. Biom J 2021; 63:875-892. [PMID: 33491802 DOI: 10.1002/bimj.201900404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 11/09/2022]
Abstract
Probabilistic approaches to hazard assessment use species sensitivity distributions (SSDs) to characterize hazard for toxicants exposure for different species within a community. Many of the assumptions at the core of SSDs are unrealistic, among them the assumption that the tolerance levels of all species in a specific ecological community are a priori exchangeable for each new toxic substance. Here we propose the use of a particular test to detect situations where such an assumption is violated. Then, a new method based on non-nested random effects model is required to identify novel SSDs capable of taking into account species non-exchangeability. Credible intervals, representing SSD uncertainty, could be determined based on our procedure. This leads to new and reliable estimates of the environmental hazard. We present a Bayesian modeling approach to address model inference issues, using Markov chain Monte Carlo sampling.
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Affiliation(s)
- Sonia Migliorati
- Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
| | - Gianna Serafina Monti
- Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
| | - Marco Vighi
- Department of Environmental Sciences, University of Milano-Bicocca, Milan, Italy
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7
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Nagamalla L, Kumar JVS. In silico screening of FDA approved drugs on AXL kinase and validation for breast cancer cell line. J Biomol Struct Dyn 2020; 39:2056-2070. [PMID: 32178589 DOI: 10.1080/07391102.2020.1742791] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
AXL kinase has been over expressed in many tumors and its involvement in cell proliferation, migration, survival, and resistance makes the kinase as attractive therapeutic target for many cancers. In this study, we performed a virtual screening of the food and drug administration (FDA) approved drug molecule database against AXL kinase for repurposing studies of breast cancer. We have identified three non-cancer drugs with good binding energies were subjected to in vitro breast cancer MCF-7 cell lines. Three drug molecules showing the activity with good IC50 values toward the cancer cell line. We also carried out a 2 dimensional (2 D) quantitative structure activity relation (QSAR) studies on N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides derivatives to design potent inhibitors for AXL kinase. The final QSAR equation was robust with good predictivity and the statistical validation having R2 and Q2 values are 0.91 and 0.86, respectively. QSAR equation descriptors informs about the chemical properties of AXL inhibitors and helpful for designing novel inhibitors. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lavanya Nagamalla
- Department of Chemistry, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
| | - J V Shanmukha Kumar
- Department of Chemistry, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
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Chinta S, Rengaswamy R. Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04584] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sivadurgaprasad Chinta
- Department of Chemical Engineering, Robert Bosch Center for Data Science and AI, Indian Institute of Technology Madras, Chennai, Tamil Nadu India -600036
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering, Robert Bosch Center for Data Science and AI, Indian Institute of Technology Madras, Chennai, Tamil Nadu India -600036
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9
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Ghamali M, Chtita S, Ousaa A, Elidrissi B, Bouachrine M, Lakhlifi T. QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN. JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2018. [DOI: 10.1016/j.jtusci.2016.03.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Mounir Ghamali
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail MeknesMorocco
| | - Samir Chtita
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail MeknesMorocco
| | - Abdellah Ousaa
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail MeknesMorocco
| | - Bouhya Elidrissi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail MeknesMorocco
| | | | - Tahar Lakhlifi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail MeknesMorocco
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Ranjan P, Athar M, Jha PC, Krishna KV. Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling. Mol Divers 2018; 22:669-683. [PMID: 29611020 DOI: 10.1007/s11030-018-9825-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 03/16/2018] [Indexed: 12/30/2022]
Abstract
A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.
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Affiliation(s)
- Prabodh Ranjan
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Mohd Athar
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Prakash Chandra Jha
- CCG@CUG, Centre for Applied Chemistry, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India.
| | - Kari Vijaya Krishna
- Department of Chemistry, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, 632014, India
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11
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Hamadache M, Benkortbi O, Hanini S, Amrane A. QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:896-907. [PMID: 29067614 DOI: 10.1007/s11356-017-0498-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/16/2017] [Indexed: 06/07/2023]
Abstract
Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q 2 and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.
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Affiliation(s)
- Mabrouk Hamadache
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria.
| | - Othmane Benkortbi
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Salah Hanini
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Abdeltif Amrane
- Ecole Nationale Supérieure de Chimie de Rennes, CNRS, UMR 6226, Université de Rennes 1, 11 allée de Beaulieu, 35708, Rennes Cedex 7, CS 50837, France
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Shamsara J. Ezqsar: An R Package for Developing QSAR Models Directly From Structures. THE OPEN MEDICINAL CHEMISTRY JOURNAL 2017; 11:212-221. [PMID: 29387275 PMCID: PMC5748834 DOI: 10.2174/1874104501711010212] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 11/01/2017] [Accepted: 11/12/2017] [Indexed: 02/01/2023]
Abstract
Background: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers. Method and Materials: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated. Results: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example. Conclusion: The R package, ezqsar, is freely available viahttps://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.
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Affiliation(s)
- Jamal Shamsara
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
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Živković JV, Trutić NV, Veselinović JB, Nikolić GM, Veselinović AM. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. Comput Biol Med 2015; 64:276-82. [DOI: 10.1016/j.compbiomed.2015.07.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 06/28/2015] [Accepted: 07/07/2015] [Indexed: 12/23/2022]
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