The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

Author(s): Maja Zivkovic, Marko Zlatanovic, Nevena Zlatanovic, Mladjan Golubović, Aleksandar M. Veselinović*

Journal Name: Mini-Reviews in Medicinal Chemistry

Volume 20 , Issue 14 , 2020

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Graphical Abstract:


In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

Keywords: Monte Carlo method, QSAR, SMILES, optimal descriptor, Molecular docking, Drug design.

Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br. J. Pharmacol., 2007, 152(1), 9-20.
[] [PMID: 17549047]
Tabeshpour, J.; Sahebkar, A.; Zirak, M.R.; Zeinali, M.; Hashemzaei, M.; Rakhshani, S.; Rakhshani, S. Computer-aided drug design and drug pharmacokinetic prediction: A mini-review. Curr. Pharm. Design, 24(26), 3014-3019.
Terstappen, G.C.; Reggiani, A. In silico research in drug discovery. Trends Pharmacol. Sci., 2001, 22(1), 23-26.
[] [PMID: 11165668]
Dudek, A.Z.; Arodz, T.; Gálvez, J. Computational methods in developing quantitative structure-activity relationships (QSAR): A review. Comb. Chem. High Throughput Screen., 2006, 9(3), 213-228.
[] [PMID: 16533155]
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[] [PMID: 27463326]
Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Naenna, T.; Prachayasittikul, V. A practical overview of quantitative structure activity relationship. EXCLI J., 2009, 8, 74-88.
Du, Q-S.; Huang, R-B.; Chou, K-C. Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr. Protein Pept. Sci., 2008, 9(3), 248-260.
[] [PMID: 18537680]
Scior, T.; Medina-Franco, J.L.; Do, Q-T.; Martínez-Mayorga, K.; Yunes Rojas, J.A.; Bernard, P. How to recognize and workaround pitfalls in QSAR studies: A critical review. Curr. Med. Chem., 2009, 16(32), 4297-4313.
[] [PMID: 19754417]
González, M.P.; Terán, C.; Saíz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: An overview. Curr. Top. Med. Chem., 2008, 8(18), 1606-1627.
[] [PMID: 19075770]
Liu, P.; Long, W. Current mathematical methods used in QSAR/QSPR studies. Int. J. Mol. Sci., 2009, 10(5), 1978-1998.
[] [PMID: 19564933]
Walker, J.D.; Jaworska, J.; Comber, M.H.I.; Schultz, T.W.; Dearden, J.C. Guidelines for developing and using quantitative structure-activity relationships. Environ. Toxicol. Chem., 2003, 22(8), 1653-1665.
[] [PMID: 12924568]
Yang, G-F.; Huang, X. Development of quantitative structure activity relationships and its application in rational drug design. Curr. Pharm. Des., 2006, 12(35), 4601-4611.
[] [PMID: 17168765]
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; Consonni, V.; Kuz’min, V.E.; 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(12), 4977-5010.
[] [PMID: 24351051]
Perkins, R.; Fang, H.; Tong, W.; Welsh, W.J. Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology. Environ. Toxicol. Chem., 2003, 22(8), 1666-1679.
[] [PMID: 12924569]
Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Advances in computational methods to predict the biological activity of compounds. Expert Opin. Drug Discov., 2010, 5(7), 633-654.
[] [PMID: 22823204]
Grover, I.; Singh, I.; Bakshi, I.; Singh, S. Quantitative structure property relationships in pharmaceutical research - Part 1. Pharm. Sci. Technol. Today, 2000, 3(1), 28-35.
[] [PMID: 10637598]
Gálvez, J.; García-Doménech, R. On the contribution of molecular topology to drug design and discovery. Curr Comput Aided Drug Des, 2010, 6(4), 252-268.
[] [PMID: 20883200]
Gozalbes, R.; Doucet, J.P.; Derouin, F. Application of topological descriptors in QSAR and drug design: History and new trends. Curr. Drug Targets Infect. Disord., 2002, 2(1), 93-102.
[] [PMID: 12462157]
Gálvez, J.; Gálvez-Llompart, M.; García-Domenech, R. Molecular topology as a novel approach for drug discovery. Expert Opin. Drug Discov., 2012, 7(2), 133-153.
[] [PMID: 22468915]
Zanni, R.; Galvez-Llompart, M.; García-Domenech, R.; Galvez, J. Latest advances in molecular topology applications for drug discovery. Expert Opin. Drug Discov., 2015, 10(9), 945-957.
[] [PMID: 26134383]
Helguera, A.M.; Combes, R.D.; González, M.P.; Cordeiro, M.N.D.S. Applications of 2D descriptors in drug design: A DRAGON tale. Curr. Top. Med. Chem., 2008, 8(18), 1628-1655.
[] [PMID: 19075771]
Roy, K.; Das, R.N. A review on principles, theory and practices of 2D-QSAR. Curr. Drug Metab., 2014, 15(4), 346-379.
[] [PMID: 25204823]
Akamatsu, M. Current state and perspectives of 3D-QSAR. Curr. Top. Med. Chem., 2002, 2(12), 1381-1394.
[] [PMID: 12470286]
Kubinyi, H. QSAR and 3D QSAR in drug design. Part 1: Methodology. Drug Discov. Today, 1997, 2(11), 457-467.
Kubinyi, H. QSAR and 3D QSAR in drug design part 2: Applications and problems. Drug Discov. Today, 1997, 2(12), 538-546.
Arakawa, M.; Hasegawa, K.; Funatsu, K. The recent trend in QSAR modeling - Variable selection and 3D-QSAR methods. Curr. Comput. Aided Drug, 2007, 3(4), 254-262.
Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design--A review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[] [PMID: 19929826]
Lemmen, C.; Lengauer, T. Computational methods for the structural alignment of molecules. J. Comput. Aided Mol. Des., 2000, 14(3), 215-232.
[] [PMID: 10756477]
Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 2010, 50(7), 1189-1204.
[] [PMID: 20572635]
Shahlaei, M. Descriptor selection methods in quantitative structure activity relationship studies: A review study. Chem. Rev., 2013, 113(10), 8093-8103.
[] [PMID: 23822589]
Mitchell, J.B.O. Machine learning methods in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2014, 4(5), 468-481.
[] [PMID: 25285160]
Veselinović, A.M.; Veselinović, J.B.; Živković, J.V.; Nikolić, G.M. Application of SMILES notation based optimal descriptors in drug discovery and design. Curr. Top. Med. Chem., 2015, 15(18), 1768-1779.
[] [PMID: 25961525]
Toropova, A.P.; Toropov, A.A. CORAL: Monte Carlo method to predict endpoints for medical chemistry. Mini Rev. Med. Chem., 2018, 18(5), 382-391.
[] [PMID: 28971771]
Toropova, M.A.; Raška, I., Jr; Toropov, A.A.; Rašková, M. The utilization of the Monte Carlo technique for rational drug discovery. Comb. Chem. High Throughput Screen., 2016, 19(8), 676-687.
[] [PMID: 27457244]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Nicolotti, O.; Carotti, A.; Nesmerak, K.; Veselinovic, A.M.; Veselinovic, J.B.; Duchowicz, P.R.; Bacelo, D.E.; Castro, E.A.; Rasulev, B.F.; Leszczynska, D.; Leszczynski, J. QSPR/QSAR analyses by means of the CORAL software: Results, challenges, perspectives. Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment; Roy, K; Ed.; IGI Global. , 2015, pp. 560-585.
Toropova, A.P.; Toropov, A.A.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. Virtual screening of anti-cancer compounds: Application of monte carlo technique. Anti-. Anticancer. Agents Med. Chem., 2019, 19(2), 148-153.
[] [PMID: 30360729]
Toropova, M.A.; Raska, I., Jr; Toporova, A.P.; Raskova, M. CORAL software: Analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors. Curr. Drug Metab., 2017, 18(6), 500-510.
[] [PMID: 28260514]
Ahmadi, S.; Mardinia, F.; Azimi, N.; Qomi, M.; Balali, E. Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method. J. Mol. Struct., 2019, 1181, 305-311.
Sokolović, D.; Stanković, V.; Toskić, D.; Lilić, L.; Ranković, G.; Ranković, J.; Nedin-Ranković, G.; Veselinović, A.M. Monte Carlo based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct. Chem., 2016, 27(5), 1511-1519.
Ahmadi, S.; Akbari, A. Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method. SAR QSAR Environ. Res., 2018, 29(11), 895-909.
[] [PMID: 30332923]
Toropov, A.A.; Toropova, A.P.; Roncaglioni, A.; Benfenati, E. Prediction of biochemical endpoints by the coral software: Prejudices, paradoxes, and Results. Computational Toxicology. Methods in Molecular Biology; Orazio, N., Ed.; Humana Press: New York, 2018, Vol. 1800, pp. 573-583.
Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 2002, 47(4), 409-443.
[] [PMID: 12001221]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
[] [PMID: 15520816]
Brooijmans, N.; Kuntz, I.D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32, 335-373.
[] [PMID: 12574069]
Sousa, S.F.; Fernandes, P.A.; Ramos, M.J. Protein-ligand docking: Current status and future challenges. Proteins, 2006, 65(1), 15-26.
[] [PMID: 16862531]
Pinzi, L.; Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 2019, 20(18), 4331.
[] [PMID: 31487867]
Torres, P.H.M.; Sodero, A.C.R.; Jofily, P.; Silva-Jr, F.P. Key topics in molecular docking for drug design. Int. J. Mol. Sci., 2019, 20(18), 4574.
[] [PMID: 31540192]
Gramatica, P.; Chirico, N.; Papa, E.; Cassani, S.; Kovarich, S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. J. Comput. Chem., 2013, 34(24), 2121-2132.
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Fanelli, R. The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method. Struct. Chem., 2013, 24(4), 1369-1381.
Martin, T.M.; Harten, P.; Young, D.M.; Muratov, E.N.; 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(10), 2570-2578.
[] [PMID: 23030316]
Puzyn, T.; Mostrag-Szlichtyng, A.; Gajewicz, A.; Skrzyński, M.; Worth, A.P. Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models. Struct. Chem., 2011, 22(4), 795-804.
Khan, P.M.; Baderna, D.; Lombardo, A.; Roy, K.; Benfenati, E. Chemometric modeling to predict air half-life of Persistent Organic Pollutants (POPs). J. Hazard. Mater., 2020., 382121035.
[] [PMID: 31450211]
Ambure, P.; Gajewicz-Skretna, A.; Cordeiro, M.N.D.S.; Roy, K. New workflow for QSAR model development from small data sets: Small dataset curator and small dataset modeler. Integration of data curation, exhaustive double cross-validation, and a set of optimal model selection techniques. J. Chem. Inf. Model., 2019, 59(10), 4070-4076.
[] [PMID: 31525295]
Veselinović, J.B.; Veselinović, A.M.; Toropova, A.P.; Toropov, A.A. The Monte Carlo technique as a tool to predict LOAEL. Eur. J. Med. Chem., 2016, 116, 71-75.
[] [PMID: 27060758]
Toropov, A.A.; Toropova, A.P.; Pizzo, F.; Lombardo, A.; Gadaleta, D.; Benfenati, E. CORAL: Model for no observed adverse effect level (NOAEL). Mol. Divers., 2015, 19(3), 563-575.
[] [PMID: 25850638]
Toropova, A.P.; Toropov, A.A.; Veselinović, J.B.; Veselinović, A.M. QSAR as a random event: A case of NOAEL. Environ. Sci. Pollut. Res. Int., 2015, 22(11), 8264-8271.
[] [PMID: 25520208]
Randić, M. Novel shape descriptors for molecular graphs. J. Chem. Inf. Comput. Sci., 2001, 41(3), 607-613.
[] [PMID: 11410036]
Randić, M. On history of the Randić index and emerging hostility toward chemical graph theory. Match (Mulh.), 2008, 59(1), 5-124.
Toropov, A.A.; Toropova, A.P. Modeling of lipophilicity by means of correlation weighting of local graph invariants. J. Mol. Struct. THEOCHEM, 2001, 538(1-3), 197-199.
Toropov, A.A.; Toropova, A.P. Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants. J. Mol. Struct. Theochem, 2001, 538(1-3), 287-293.
Krenkel, G.; Castro, E.A.; Toropov, A.A. Improved molecular descriptors to calculate boiling points based on the optimization of correlation weights of local graph invariants. J. Mol. Struct. THEOCHEM, 2001, 542(1-3), 107-113.
Stoičkov, V.; Stojanović, D.; Tasić, I.; Šarić, S.; Radenković, D.; Babović, P.; Sokolović, D.; Veselinović, A.M. QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method. Struct. Chem., 2018, 29(2), 441-449.
Toropov, A.A.; Duchowicz, P.; Castro, E.A. Structure-Toxicity relationships for aliphatic compounds based on correlation weighting of local graph invariants. Int. J. Mol. Sci., 2003, 4(5), 272-283.
Weininger, D. SMILES, a Chemical Language and Information System: 1: Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 1988, 28(1), 31-36.
Weininger, D.; Weininger, A.; Weininger, J.L. SMILES. 2. Algorithm for Generation of Unique SMILES Notation. J. Chem. Inf. Comput. Sci., 1989, 29(2), 97-101.
Toropov, A.A.; Toropova, A.P.; Raska, I., Jr; Benfenati, E.; Gini, G. QSAR modeling of endpoints for peptides which is based on representation of the molecular structure by a sequence of amino acids. Struct. Chem., 2012, 23(6), 1891-1904.
Veselinović, A.M.; Milosavljević, J.B.; Toropov, A.A.; Nikolić, G.M. SMILES-based QSAR model for arylpiperazines as high affinity 5-HT(1A) receptor ligands using CORAL. Eur. J. Pharm. Sci., 2013, 48(3), 532-541.
[] [PMID: 23287365]
Veselinović, A.M.; Milosavljević, J.B.; Toropov, A.A.; Nikolić, G.M. SMILES-based QSAR models for the calcium channel antagonistic effect of 1,4-dihydropyridines. Arch. Pharm. (Weinheim), 2013, 346(2), 134-139.
[] [PMID: 23280520]
Toropov, A.A.; Toropova, A.P.; Lombardo, A.; Roncaglioni, A.; De Brita, N.; Stella, G.; Benfenati, E. CORAL: The prediction of biodegradation of organic compounds with optimal SMILES-based descriptors. Cent. Eur. J. Chem., 2012, 10(4), 1042-1048.
Toropova, A.P.; Toropov, A.A.; Marzo, M.; Escher, S.E.; Dorne, J.L.; Georgiadis, N.; Benfenati, E. The application of new HARD descriptor available from the CORAL software to building up NOAEL models. Food Chem. Toxicol., 2018, 112, 544-550.
[] [PMID: 28366846]
Kumar, P.; Kumar, A.; Sindhu, J. In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR QSAR Environ. Res., 2019, 30(8), 525-541.
[] [PMID: 31331203]
Toropov, A.A.; Toropova, A.P.; Marzo, M.; Dorne, J.L.; Georgiadis, N.; Benfenati, E. QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database. Environ. Toxicol. Pharmacol., 2017, 53, 158-163.
[] [PMID: 28599185]
Toropova, A.P.; Toropov, A.A.; Benfenati, E. CORAL: Prediction of binding affinity and efficacy of thyroid hormone receptor ligands. Eur. J. Med. Chem., 2015, 101, 452-461.
[] [PMID: 26188619]
Toropov, A.A.; Veselinović, J.B.; Veselinović, A.M.; Miljković, F.N.; Toropova, A.P. QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method. Med. Chem. Res., 2015, 24(1), 283-290.
Veselinović, J.B.; Toropov, A.A.; Toropova, A.P.; Nikolić, G.M.; Veselinović, A.M. Monte carlo method-based QSAR modeling of penicillins binding to human serum proteins. Arch. Pharm. (Weinheim), 2015, 348(1), 62-67.
[] [PMID: 25408278]
Toropov, A.A.; Toropova, A.P.; Rasulev, B.F.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. CORAL: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical. J. Comput. Chem., 2012, 33(23), 1902-1906.
[] [PMID: 22641453]
Roy, K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin. Drug Discov., 2007, 2(12), 1567-1577.
[] [PMID: 23488901]
Ojha, P.K.; Mitra, I.; Das, R.N.; Roy, K. Further exploring rm2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst., 2011, 107(1), 194-205.
Roy, P.P.; Leonard, J.T.; Roy, K. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemom. Intell. Lab. Syst., 2008, 90(1), 31-42.
Roy, K.; Das, R.N.; Ambure, P.; Aher, R.B. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr. Intell. Lab., 2016, 152, 18-33.
Roy, K.; Ambure, P.; Kar, S.; Ojha, P.K. Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J. Chemometr., 2018, 32(4), e2992.
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[] [PMID: 11858635]
Ojha, P.K.; Roy, K. Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection. Chemom. Intell. Lab. Syst., 2011, 109(2), 146-161.
Toropov, A.A.; Carbó-Dorca, R.; Toropova, A.P. Index of Ideality of Correlation: New possibilities to validate QSAR: A case study. In: Struct. Chem., 2017, 29(1), 33-38.
Toropova, A.P.; Toropov, A.A. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Sci. Total Environ., 2017, 586, 466-472.
[] [PMID: 28196626]
Toropov, A.A.; Toropova, A.P.; Selvestrel, G.; Benfenati, E. Idealization of correlations between optimal simplified molecular input line entry system-based descriptors and skin sensitization. SAR QSAR Environ. Res., 2019, 30(6), 447-455.
[] [PMID: 31124730]
Toropov, A.A.; Raška, I., Jr; Toropova, A.P.; Raškova, M.; Veselinović, A.M.; Veselinović, J.B. The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models. Sci. Total Environ., 2019, 659, 1387-1394.
[] [PMID: 31096349]
Kumar, P.; Kumar, A.; Sindhu, J. Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR QSAR Environ. Res., 2019, 30(2), 63-80.
[] [PMID: 30793981]
Kumar, P.; Kumar, A. Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method. J. Biomol. Struct. Dyn., 2019, 1-11.
[] [PMID: 31411551]
Toropova, A.P.; Toropov, A.A. Does the Index of Ideality of Correlation Detect the Better Model Correctly?; Mol. Inform, 2019.
[] [PMID: 30725522]
Jain, S.; Amin, S.A.; Adhikari, N.; Jha, T.; Gayen, S. Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: Identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study. J. Biomol. Struct. Dyn., 2019.
[] [PMID: 30646829]
Weaver, S.; Gleeson, M.P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model., 2008, 26(8), 1315-1326.
[] [PMID: 18328754]
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci., 2007, 26(5), 694-701.
Gadaleta, D.; Mangiatordi, G.F.; Catto, M.; Carotti, A.; Nicolotti, O. Applicability domain for QSAR models: Where theory meets reality. IJQSPR, 2016, 1(1), 45-63.
Roy, K.; Ambure, P.; Kar, S. How precise are our quantitative structure-activity relationship derived predictions for new query chemicals? ACS Omega, 2018, 3(9), 11392-11406.
[] [PMID: 31459245]
Kar, S.; Roy, K.; Leszczynski, J. Applicability domain: A step toward confident predictions and decidability for QSAR modeling. Computational Toxicology. Methods in Molecular Biology; Orazio, N., Ed.; Humana Press: New York, 2018, Vol. 1800, pp. 141-169.
Roy, K.; Ambure, P.; Aher, R.B. How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemom. Intell. Lab. Syst., 2017, 162, 44-54.
Gramatica, P.; Sangion, A. A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology. J. Chem. Inf. Model., 2016, 56(6), 1127-1131.
[] [PMID: 27218604]
Toropov, A.A.; Benfenati, E. Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain. Bioorg. Med. Chem., 2008, 16(9), 4801-4809.
[] [PMID: 18395455]
Toropov, A.A.; Toropova, A.P.; Lombardo, A.; Roncaglioni, A.; Benfenati, E.; Gini, G. CORAL: Building up the model for bioconcentration factor and defining it’s applicability domain. Eur. J. Med. Chem., 2011, 46(4), 1400-1403.
[] [PMID: 21295893]
Roy, K.; Mitra, I. On the use of the metric rm2 as an effective tool for validation of QSAR models in computational drug design and predictive toxicology. Mini-Rev. Med. Chem., 2012, 12(6), 491-504.
[] [PMID: 22587764]
Veselinović, J.B.; Kocić, G.M.; Pavic, A.; Nikodinovic-Runic, J.; Senerovic, L.; Nikolić, G.M.; Veselinović, A.M. Selected 4-phenyl hydroxycoumarins: In vitro cytotoxicity, teratogenic effect on zebrafish (Danio rerio) embryos and molecular docking study. Chem. Biol. Interact., 2015, 231, 10-17.
[] [PMID: 25724286]
Thomsen, R.; Christensen, M.H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[] [PMID: 16722650]
Veselinović, A.M.; Toropov, A.; Toropova, A.; Stanković-Dordević, D.; Veselinović, J.B. Design and development of novel antibiotics based on FtsZ inhibition - In silico studies. New J. Chem., 2018, 42(13), 10976-10982.
Amin, S.A.; Adhikari, N.; Gayen, S.; Jha, T. Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: A predictive approach combining comparative validated multiple molecular modelling techniques. J. Biomol. Struct. Dyn., 2017, 36(3), 590-608.
Amin, S.A.; Adhikari, N.; Gayen, S.; Jha, T. Reliable structural information for rational design of benzoxazole type potential cholesteryl ester transfer protein (CETP) inhibitors through multiple validated modelling techniques. J. Biomol. Struct. Dyn., 2018.
[] [PMID: 30488780]
Amin, S.A.; Bhattacharya, P.; Basak, S.; Gayen, S.; Nandy, A.; Saha, A. Pharmacoinformatics study of Piperolactam A from Piper betle root as new lead for non steroidal anti fertility drug development. Comput. Biol. Chem., 2017, 67, 213-224.
[] [PMID: 28160639]
Veselinovic, J.; Veselinovic, A.; Toropov, A.; Toropova, A.; Damnjanovic, I.; Nikolic, G. Monte carlo method based QSAR modeling of coumarin derivates as potent HIV-1 integrase inhibitors and molecular docking studies of selected 4-phenyl hydroxycoumarins. Acta Fac. Med. Naiss., 2014, 31(2), 95-103.
Simon, L.; Imane, A.; Srinivasan, K.K.; Pathak, L.; Daoud, I. In silico drug-designing studies on flavanoids as anticolon cancer agents: Pharmacophore mapping, molecular docking, and monte carlo method-based QSAR modeling. Interdiscip. Sci., 2017, 9(3), 445-458.
[] [PMID: 27059855]
Stoičkov, V.; Šarić, S.; Golubović, M.; Zlatanović, D.; Krtinić, D.; Dinić, L.; Mladenović, B.; Sokolović, D.; Veselinović, A.M. Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach. SAR QSAR Environ. Res., 2018, 29(7), 503-515.
[] [PMID: 30058413]
Manisha; Chauhan, S.; Kumar, P.; Kumar, A. Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method. SAR QSAR Environ. Res., 2019, 30(3), 145-159.
[] [PMID: 30777782]
Ničković, V.P.; Mitić, N.R.; Krdžić, B.D.; Krdžić, J.D.; Nikolić, G.R.; Vasić, M.Z.; Ranković, G.; Babović, P.; Sokolović, D.; Veselinović, A.M. Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition. J. Biomol. Struct. Dyn., 2019, 1-10.
[] [PMID: 31096856]
Ćirić Zdravković, S.; Pavlović, M.; Apostlović, S.; Koraćević, G.; Šalinger Martinović, S.; Stanojević, D.; Sokolović, D.; Veselinović, A.M. Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition-In silico approach. Comput. Biol. Chem., 2019, 79, 55-62.
[] [PMID: 30716601]
Bhargava, S.; Patel, T.; Gaikwad, R.; Patil, U.K.; Gayen, S. Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR Simulation. Nat. Prod. Res., 2019, 33(6), 851-857.
[] [PMID: 29241370]
Ničković, V.P.; Vujnović-Živković, Z.N.; Trajković, R.; Krtinić, D.; Ristić, L.; Radović, M.; Ćirić, Z.; Sokolović, D.; Veselinović, A.M. In silico studies and the design of novel agents for the treatment of systemic tuberculosis. J. Biomol. Struct. Dyn., 2019, 37(12), 3198-3205.
[] [PMID: 30099932]

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Article Details

Year: 2020
Published on: 01 September, 2020
Page: [1389 - 1402]
Pages: 14
DOI: 10.2174/1389557520666200212111428
Price: $65

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