Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Author(s): Alla P. Toropova*, Andrey A. Toropov*

Journal Name: Current Topics in Medicinal Chemistry

Volume 19 , Issue 29 , 2019

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Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

Keywords: QSPR/QSAR, Monte Carlo method, CORAL software, Index of ideality of correlation, Correlation contradiction index, Validation.

Dain, J.G.; Collins, J.M.; Robinson, W.T. A regulatory and industrial perspective of the use of carbon-14 and tritium isotopes in human ADME studies. Pharm. Res., 1994, 11(6), 925-928.
[http://dx.doi.org/10.1023/A:1018958631158] [PMID: 7937538]
Campbell, D.B. Are we doing too many animal biodisposition investigations before phase I studies in man? A re-evaluation of the timing and extent of ADME studies. Eur. J. Drug Metab. Pharmacokinet., 1994, 19(3), 283-293.
[http://dx.doi.org/10.1007/BF03188932] [PMID: 7867672]
Chasseaud, L.F. Accelerating ADME studies: A novel quantitative method for determining the biodistribution of radiolabelled xenobiotics using whole-body cryosectioning and autoradioluminog raphy. Potchoiba, M.J.; Tensfeldt, T.G.; Nocerini, M.R.; Silber, B.M. J. Pharmacol. Exp. Ther., 1995, 272, 953-962. Hum. Exp. Toxicol., 1995, 14(12), 991-992.
[http://dx.doi.org/10.1177/096032719501401207] [PMID: 8962749]
Scotti, L.; Yarla, N.S.; Mendonça Filho, F.J.B.; Barbosa Filho, J.M.; da Silva, M.S.; Tavares, J.F.; Scotti, M.T. CADD studies applied to secondary metabolites in the anticancer drug research. In: Anticancer Plants: Mechanisms and Molecular Interactions, Akhtar, M.S.; Swamy, M.K.; Eds.; Springer Nature Singapore Pte Ltd; , 2018; 4, pp. 209-225.
Ferreira, L.L.G.; Andricopulo, A.D. ADMET modeling approaches in drug discovery. Drug Discov. Today, 2019, 24(5), 1157-1165.
[http://dx.doi.org/10.1016/j.drudis.2019.03.015] [PMID: 30890362]
Aouidate, A.; Ghaleb, A.; Ghamali, M.; Chtita, S.; Ousaa, A.; Choukrad, M.; Sbai, A.; Bouachrine, M.; Lakhlifi, T. Investigation of indirubin derivatives: a combination of 3D-QSAR, molecular docking, and ADMET towards the design of new DRAK2 inhibitors. Struct. Chem., 2018, 29(6), 1609-1622.
Alam, S.; Khan, F. Virtual screening, Docking, ADMET and System Pharmacology studies on Garcinia caged Xanthone derivatives for Anticancer activity. Sci. Rep., 2018, 8(1), 5524.
[http://dx.doi.org/10.1038/s41598-018-23768-7] [PMID: 29615704]
Aouidate, A.; Ghaleb, A.; Ghamali, M.; Chtita, S.; Ousaa, A.; Choukrad, M.; Sbai, A.; Bouachrine, M.; Lakhlifi, T. Furanone derivatives as new inhibitors of CDC7 kinase: development of structure activity relationship model using 3D QSAR, molecular docking, and in silico ADMET. Struct. Chem., 2018, 29(4), 1031-1043.
Rocha, J.A.; Rego, N.C.S.; Carvalho, B.T.S.; Silva, F.I.; Sousa, J.A.; Ramos, R.M.; Passos, I.N.G.; de Moraes, J.; Leite, J.R.S.A.; Lima, F.C.A. Computational quantum chemistry, molecular docking, and ADMET predictions of imidazole alkaloids of Pilocarpus microphyllus with schistosomicidal properties. PLoS One, 2018, 13(6)e0198476
[http://dx.doi.org/10.1371/journal.pone.0198476] [PMID: 29944674]
James, J.P.; Ishwar Bhat, K.; More, U.A.; Joshi, S.D. Design, synthesis, molecular modeling, and ADMET studies of some pyrazoline derivatives as shikimate kinase inhibitors. Med. Chem. Res., 2018, 27(2), 546-559.
Schyman, P.; Liu, R.; Desai, V.; Wallqvist, A. vNN web server for ADMET predictions. Front. Pharmacol., 2017, 8, 889.
[http://dx.doi.org/10.3389/fphar.2017.00889] [PMID: 29255418]
Speck-Planche, A.; Cordeiro, M.N.D.S. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med. Chem. Res., 2017, 26(10), 2345-2356.
Speck-Planche, A.; Cordeiro, M.N. Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin. Drug Discov., 2015, 10(3), 245-256.
[http://dx.doi.org/10.1517/17460441.2015.1006195] [PMID: 25613725]
Speck-Planche, A.; Cordeiro, M.N.D.S. Advanced in silico approaches for drug discovery: mining information from multiple biological and chemical data through mtk- QSBER and pt-QSPR Strategies. Curr. Med. Chem., 2017, 24(16), 1687-1704.
[http://dx.doi.org/10.2174/0929867324666170124152746] [PMID: 28120706]
Sanders, J.M.; Beshore, D.C.; Culberson, J.C.; Fells, J.I.; Imbriglio, J.E.; Gunaydin, H.; Haidle, A.M.; Labroli, M.; Mattioni, B.E.; Sciammetta, N.; Shipe, W.D.; Sheridan, R.P.; Suen, L.M.; Verras, A.; Walji, A.; Joshi, E.M.; Bueters, T. Informing the selection of screening hit series with in silico absorption, distribution, metabolism, excretion, and toxicity profiles. J. Med. Chem., 2017, 60(16), 6771-6780.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01577] [PMID: 28418656]
Toropova, M.A. Drug metabolism as an object of computational analysis by the Monte Carlo method. Curr. Drug Metab., 2017, 18(12), 1123-1131.
[http://dx.doi.org/10.2174/1389200218666171010124733] [PMID: 29032749]
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.
[http://dx.doi.org/10.2174/1389200218666170301105916] [PMID: 28260514]
Toropova, M.A.; Raška, I.; 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.
[http://dx.doi.org/10.2174/1386207319666160725145852] [PMID: 27457244]
Gobbi, M.; Beeg, M.; Toropova, M.A.; Toropov, A.A.; Salmona, M. Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds. Toxicol. Lett., 2016, 250-251, 42-46.
[http://dx.doi.org/10.1016/j.toxlet.2016.04.010] [PMID: 27067105]
Toropova, M.A.; Toropov, A.A.; Raška, I., Jr; Rašková, M. Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Comput. Biol. Med., 2015, 64, 148-154.
[http://dx.doi.org/10.1016/j.compbiomed.2015.06.019] [PMID: 26164035]
Alexander, D.L.J.; Tropsha, A.; Winkler, D.A. Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. J. Chem. Inf. Model., 2015, 55(7), 1316-1322.
[http://dx.doi.org/10.1021/acs.jcim.5b00206] [PMID: 26099013]
Wiener, H. Structural determination of paraffin boiling points. J. Am. Chem. Soc., 1947, 69(1), 17-20.
[http://dx.doi.org/10.1021/ja01193a005] [PMID: 20291038]
Wiener, H. Correlation of heats of isomerization, and differences in heats of vaporization of isomers, among the paraffin hydrocarbons. J. Am. Chem. Soc., 1947, 69(11), 2636-2638.
Wiener, H. Relation of the physical properties of the isomeric alkanes to molecular structure; surface, tension, specific dispersion, and critical solution temperature in aniline. J. Phys. Colloid Chem., 1948, 52(6), 1082-1089.
[http://dx.doi.org/10.1021/j150462a018] [PMID: 18867478]
Wiener, H. Vapor pressure-temperature relationships among the branched paraffin hydrocarbons. J. Phys. Colloid Chem., 1948, 52(2), 425-430.
[http://dx.doi.org/10.1021/j150458a014] [PMID: 18906414]
Hosoya, H. Topological index as a sorting device for coding chemical structures. J. Chem. Doc., 1972, 12, 181-183.
Amidon, G.L.; Anik, S.T. Comparison of several molecular topological indexes with molecular surface area in aqueous solubility estimation. J. Pharm. Sci., 1976, 65(6), 801-806.
[http://dx.doi.org/10.1002/jps.2600650603] [PMID: 932962]
Bonchev, D.; Balaban, A.T.; Mekenyan, O. Generalization of the graph center concept and derived topological centric indexes. J. Chem. Inf. Comput. Sci., 1980, 20, 106-113.
Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern. Lab. Anim., 2005, 33(5), 445-459.
[http://dx.doi.org/10.1177/026119290503300508] [PMID: 16268757]
Dimitrov, S.; Dimitrova, G.; Pavlov, T.; Dimitrova, N.; Patlewicz, G.; Niemela, J.; Mekenyan, O. A stepwise approach for defining the applicability domain of SAR and QSAR models. J. Chem. Inf. Model., 2005, 45(4), 839-849.
[http://dx.doi.org/10.1021/ci0500381] [PMID: 16045276]
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.
Hemmateenejad, B.; Javadnia, K.; Elyasi, M. Quantitative structure-retention relationship for the Kovats retention indices of a large set of terpenes: a combined data splitting-feature selection strategy. Anal. Chim. Acta, 2007, 592(1), 72-81.
[http://dx.doi.org/10.1016/j.aca.2007.04.009] [PMID: 17499073]
Hemmateenejad, B.; Javidnia, K.; Miri, R.; Elyasi, M. Quantitative structure-retention relationship study of analgesic drugs by application of combined data splitting-feature selection strategy and genetic algorithm-partial least square. J. Iran. Chem. Soc., 2012, 9(1), 53-60.
Shayanfar, A.; Shayanfar, S. Is regression through origin useful in external validation of QSAR models? Eur. J. Pharm. Sci., 2014, 59(1), 31-35.
[http://dx.doi.org/10.1016/j.ejps.2014.03.007] [PMID: 24721181]
Chirico, N.; Gramatica, P. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J. Chem. Inf. Model., 2011, 51(9), 2320-2335.
[http://dx.doi.org/10.1021/ci200211n] [PMID: 21800825]
Roy, K.; Kar, S. The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on ‘Is regression through origin useful in external validation of QSAR models?’). Eur. J. Pharm. Sci., 2014, 62, 111-114.
[http://dx.doi.org/10.1016/j.ejps.2014.05.019] [PMID: 24881556]
Lin, L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 1989, 45(1), 255-268.
[http://dx.doi.org/10.2307/2532051] [PMID: 2720055]
Toropov, A.A.; Toropova, A.P. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? Mutat. Res., 2017, 819, 31-37.
[http://dx.doi.org/10.1016/j.mrgentox.2017.05.008] [PMID: 28622828]
Toropov, A.A.; Carbó-Dorca, R.; Toropova, A.P. Index of ideality of correlation: new possibilities to validate QSAR: a case study. Struct. Chem., 2018, 29(1), 33-38.
Toropov, A.A.; Toropova, A.P. QSAR as a random event: criteria of predictive potential for a chance model. Struct. Chem., 2019, 30(5), 1677-1683.
Toropov, A.A.; Toropova, A.P. The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. Sci. Total Environ., 2019, 681, 102-109.
[http://dx.doi.org/10.1016/j.scitotenv.2019.05.114] [PMID: 31102811]
Duchowicz, P.R.; Castro, E.A.; Fernández, F.M.; Gonzalez, M.P. A new search algorithm for QSPR/QSAR theories: Normal boiling points of some organic molecules. Chem. Phys. Lett., 2005, 412(4-6), 376-380.
Toropov, A.A.; Toropova, A.P.; Gutman, I. Comparison of QSPR models based on hydrogen-filled graphs and on graphs of atomic orbitals. Croat. Chem. Acta, 2005, 78(4), 503-509.
Nikolić, S.; Milicević, A.; Trinajstić, N.; Jurić, A. On use of the variable Zagreb vM2 index in QSPR: boiling points of benzenoid hydrocarbons. Molecules, 2004, 9(12), 1208-1221.
[http://dx.doi.org/10.3390/91201208] [PMID: 18007513]
Raevsky, O.A.; Polianczyk, D.E.; Grigorev, V.Y.; Raevskaja, O.E.; Dearden, J.C. In silico prediction of aqueous solubility: a comparative study of local and global predictive models. Mol. Inform., 2015, 34(6-7), 417-430.
[http://dx.doi.org/10.1002/minf.201400144] [PMID: 27490387]
Shamsipur, M.; Hemmateenejad, B.; Ghavami, R.; Sharghi, H. Highly correlating distance-connectivity-based topological indices. 4: Stepwise factor selection-based PCR models for QSPR study of 14 properties of monoalkenes. Pol. J. Chem., 2007, 81(2), 269-294.
Shamsipur, M.; Ghavami, R.; Hemmateenejad, B.; Sharghi, H. Highly correlating distance-connectivity-based topological indices. 2: Prediction of 15 properties of a large set of alkanes using a stepwise factor selection-based PCR analysis. QSAR Comb. Sci., 2004, 23(9), 734-753.
Raevsky, O.A. Physicochemical descriptors in property-based drug design. Mini Rev. Med. Chem., 2004, 4(10), 1041-1052.
[http://dx.doi.org/10.2174/1389557043402964] [PMID: 15579112]
Toropov, A.A.; Toropova, A.P. Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes. Chem. Phys. Lett., 2018, 701, 137-146.
Duchowicz, P.R.; Vitale, M.G.; Castro, E.A. Partial Order Ranking for the aqueous toxicity of aromatic mixtures. J. Math. Chem., 2008, 44(2), 541-549.
Toropov, A.A.; Toropova, A.P.; Raska, I., Jr QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES. Eur. J. Med. Chem., 2008, 43(4), 714-740.
[http://dx.doi.org/10.1016/j.ejmech.2007.05.007] [PMID: 17629592]
Duchowicz, P.R.; Bucknum, M.J.; Castro, E.A. New molecular descriptors based upon the Euler equations for chemical graphs. J. Math. Chem., 2007, 41(2), 193-208.
Winkler, D.A.; Mombelli, E.; Pietroiusti, A.; Tran, L.; Worth, A.; Fadeel, B.; McCall, M.J. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. Toxicology, 2013, 313(1), 15-23.
[http://dx.doi.org/10.1016/j.tox.2012.11.005] [PMID: 23165187]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. CORAL: QSPR model of water solubility based on local and global SMILES attributes. Chemosphere, 2013, 90(2), 877-880.
[http://dx.doi.org/10.1016/j.chemosphere.2012.07.035] [PMID: 22921649]
Winkler, D.A. Neural networks as robust tools in drug lead discovery and development. Mol. Biotechnol., 2004, 27(2), 139-168.
[http://dx.doi.org/10.1385/MB:27:2:139] [PMID: 15208456]
Basak, S.C.; Mills, D. Quantitative structure-property relationships (QSPRs) for the estimation of vapor pressure: a hierarchical approach using mathematical structural descriptors. J. Chem. Inf. Comput. Sci., 2001, 41(3), 692-701.
[http://dx.doi.org/10.1021/ci000165r] [PMID: 11410048]
Toropov, A.A.; Toropova, A.P. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol. Mech. Methods, 2019, 29(1), 43-52.
[http://dx.doi.org/10.1080/15376516.2018.1506851] [PMID: 30064284]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Diomede, L.; Salmona, M. Use of quasi-SMILES to model biological activity of “micelle–polymer” samples. Struct. Chem., 2018, 29(4), 1213-1223.
Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol. Environ. Saf., 2016, 124, 32-36.
[http://dx.doi.org/10.1016/j.ecoenv.2015.09.038] [PMID: 26452192]
Toropov, A.A.; Rallo, R.; Toropova, A.P. Use of Quasi-SMILES and monte carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials. Curr. Top. Med. Chem., 2015, 15(18), 1837-1844.
[http://dx.doi.org/10.2174/1568026615666150506152000] [PMID: 25961527]
Devillers, J.; Mombelli, E. Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes. SAR QSAR Environ. Res., 2010, 21(7-8), 771-783.
[http://dx.doi.org/10.1080/1062936X.2010.528961] [PMID: 21120761]
Toropova, A.P.; Toropov, A.A. CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Comput. Biol. Chem., 2018, 72, 26-32.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.12.012] [PMID: 29310001]
Toropova, A.P.; Toropov, A.A. CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur. J. Pharm. Sci., 2014, 52(1), 21-25.
[http://dx.doi.org/10.1016/j.ejps.2013.10.005] [PMID: 24514451]
Helguera, A.M.; Pérez-Machado, G.; Cordeiro, M.N.D.S.; Combes, R.D. Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach. SAR QSAR Environ. Res., 2010, 21(3-4), 277-304.
[http://dx.doi.org/10.1080/10629361003773930] [PMID: 20544552]
Adhikari, N.; Amin, S.K.A.; Saha, A.; Jha, T. Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs. J. Mol. Struct., 2018, 1156, 501-515.
Concu, R.; Kleandrova, V.V.; Speck-Planche, A.; Cordeiro, M.N.D.S. Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology, 2017, 11(7), 891-906.
[http://dx.doi.org/10.1080/17435390.2017.1379567] [PMID: 28937298]
Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Leszczynska, D.; Leszczynski, J. Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna. Environ. Toxicol. Chem., 2016, 35(11), 2691-2697.
[http://dx.doi.org/10.1002/etc.3466] [PMID: 27110865]
Amin, S.A.; Adhikari, N.; Jha, T.; Gayen, S. First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: A proposal to chemists! Bioorg. Med. Chem. Lett., 2016, 26(23), 5712-5718.
[http://dx.doi.org/10.1016/j.bmcl.2016.10.058] [PMID: 27838184]
Kleandrova, V.V.; Luan, F.; Speck-Planche, A.; Cordeiro, M.N.D.S. In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect. Mini Rev. Med. Chem., 2015, 15(8), 677-686.
[http://dx.doi.org/10.2174/1389557515666150219143604] [PMID: 25694074]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Puzyn, T.; Leszczynska, D.; Leszczynski, J. Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere, 2012, 89(9), 1098-1102.
[http://dx.doi.org/10.1016/j.chemosphere.2012.05.077] [PMID: 22704203]
Toropova, A.P.; Toropov, A.A.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. CORAL: Models of toxicity of binary mixtures. Chemom. Intell. Lab. Syst., 2012, 119, 39-43.
Toropova, M.A.; Raškova, M.; Raška, I.; Toropova, A.P. The Index of Ideality of Correlation (IIC): Model for sweetness. In: Monatsh. Chem. Chem. Mon; Springer: Vienna, 2019, Vol. 15, pp. 617-623.
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.
[http://dx.doi.org/10.1016/j.scitotenv.2018.12.439] [PMID: 31096349]
Toropova, A.P.; Toropov, A.A.; Leszczynska, D.; Leszczynski, J. The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers. J. Polym. Res., 2018, 25(10), 221.
Achary, P.G.R.; Toropova, A.P.; Toropov, A.A. Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness. Food Res. Int., 2019, 122, 40-46.
[http://dx.doi.org/10.1016/j.foodres.2019.03.067] [PMID: 31229093]
Toropova, A.P.; Toropov, A.A. QSPR and nano-QSPR: What is the difference? J. Mol. Struct., 2019, 1182, 141-149.
Toropova, A.P.; Toropov, A.A. Does the index of ideality of correlation detect the better model correctly. Mol. Inform., 2019, 38(8)1800157
Toropova, A.P.; Toropov, A.A. Use of the index of ideality of correlation to improve models of eco-toxicity. Environ. Sci. Pollut. Res. Int., 2018, 25(31), 31771-31775.
[http://dx.doi.org/10.1007/s11356-018-3291-5] [PMID: 30255265]
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.
[http://dx.doi.org/10.1016/j.scitotenv.2017.01.198] [PMID: 28196626]
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.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.01.007] [PMID: 30716601]
Golubović, M.; Lazarević, M.; Zlatanović, D.; Krtinić, D.; Stoičkov, V.; Mladenović, B.; Milić, D.J.; Sokolović, D.; Veselinović, A.M. The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study. Comput. Biol. Chem., 2018, 75, 32-38.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.04.009] [PMID: 29734080]
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.
[http://dx.doi.org/10.1080/1062936X.2018.1564067] [PMID: 30793981]
Kumar, P.; Kumar, A.; Sindhu, J.; Lal, S. QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on monte carlo method. Drug Res. (Stuttg.), 2019, 69(3), 159-167.
[http://dx.doi.org/10.1055/a-0652-5290] [PMID: 30036888]
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.
Toropov, A.A.; Toropova, A.P. Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation. Anticancer Res., 2018, 38(11), 6189-6194.
[http://dx.doi.org/10.21873/anticanres.12972] [PMID: 30396936]
Stoickov, V.; Stojanovic, D.; Tasic, I.; Šaric, S.; Radenkovic, D.; Babovic, P.; Sokolovic, D.; Veselinovic, 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.
Basei, G.; Hristozov, D.; Lamon, L.; Zabeo, A.; Jeliazkova, N.; Tsiliki, G.; Marcomini, A.; Torsello, A. Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review. NanoImpact, 2019, 13, 76-99.
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Dorne, J.L. SAR for gastro-intestinal absorption and blood-brain barrier permeation of pesticides. Chem. Biol. Interact., 2018, 290, 1-5.
[http://dx.doi.org/10.1016/j.cbi.2018.04.030] [PMID: 29753609]
Doweyko, A.M. QSAR: dead or alive? J. Comput. Aided Mol. Des., 2008, 22(2), 81-89.
[http://dx.doi.org/10.1007/s10822-007-9162-7] [PMID: 18189157]
Ahmadi, S.; Ganji, S. Genetic algorithm and self-organizing maps for QSPR study of some n-aryl derivatives as butyrylcholinesterase inhibitors. Curr. Drug Discov. Technol., 2016, 13(4), 232-253.
[http://dx.doi.org/10.2174/1570163813666160725114241] [PMID: 27457492]
Primi, M.C.; Maltarollo, V.G.; Magalhães, J.G.; de Sá, M.M.; Rangel-Yagui, C.O.; Trossini, G.H.G. Convergent QSAR studies on a series of NK3 receptor antagonists for schizophrenia treatment. J. Enzyme Inhib. Med. Chem., 2016, 31(2), 283-294.
[http://dx.doi.org/10.3109/14756366.2015.1021250] [PMID: 25856571]
Wang, J.; Li, Y.; Yang, Y.; Zhang, J.; Du, J.; Zhang, S.; Yang, L. Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor. RSC Advances, 2015, 5(95), 78278-78298.
Masand, V.H.; Mahajan, D.T.; Nazeruddin, G.M.; Hadda, T.B.; Rastija, V.; Alfeefy, A.M. Effect of information leakage and method of splitting (rational and random) on external predictive ability and behavior of different statistical parameters of QSAR model. Med. Chem. Res., 2015, 24(3), 1241-1264.
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.
[http://dx.doi.org/10.1021/ci300338w] [PMID: 23030316]
Dong, Y.; Xiang, B.; Du, D.P.V. LOO-Based Training Set Selection Improves the External Predictability of QSAR/QSPR Models. J. Chem. Inf. Model., 2017, 57(5), 1055-1067.
[http://dx.doi.org/10.1021/acs.jcim.7b00029] [PMID: 28419798]
Ingle, B.L.; Veber, B.C.; Nichols, J.W.; Tornero-Velez, R. Informing the human plasma protein binding of environmental chemicals by machine learning in the pharmaceutical space: Applicability domain and limits of predictability. J. Chem. Inf. Model., 2016, 56(11), 2243-2252.
[http://dx.doi.org/10.1021/acs.jcim.6b00291] [PMID: 27684444]
Kim, M.; Li, L.Y.; Grace, J.R. Predictability of physicochemical properties of polychlorinated dibenzo-p-dioxins (PCDDs) based on single-molecular descriptor models. Environ. Pollut., 2016, 213, 99-111.
[http://dx.doi.org/10.1016/j.envpol.2016.02.007] [PMID: 26878604]
Chung, J.Y.; Cho, S.J.; Cho, A.E.; Hah, J-M. In silico binding free energy predictability with π-π interaction energy-augmented scoring function: benzimidazole Raf inhibitors as a case study. Bioorg. Med. Chem. Lett., 2012, 22(9), 3278-3283.
[http://dx.doi.org/10.1016/j.bmcl.2012.03.017] [PMID: 22464457]
Hawthorne, S.B.; Grabanski, C.B.; Miller, D.J.; Arp, H.P.H. Improving predictability of sediment-porewater partitioning models using trends observed with PCB-contaminated field sediments. Environ. Sci. Technol., 2011, 45(17), 7365-7371.
[http://dx.doi.org/10.1021/es200802j] [PMID: 21761896]
Dragos, H.; Gilles, M.; Alexandre, V. Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. J. Chem. Inf. Model., 2009, 49(7), 1762-1776.
[http://dx.doi.org/10.1021/ci9000579] [PMID: 19530661]
Arrhenius, A.; Grönvall, F.; Scholze, M.; Backhaus, T.; Blanck, H. Predictability of the mixture toxicity of 12 similarly acting congeneric inhibitors of photosystem II in marine periphyton and epipsammon communities. Aquat. Toxicol., 2004, 68(4), 351-367.
[http://dx.doi.org/10.1016/j.aquatox.2004.04.002] [PMID: 15177952]
Walker, J.D.; Carlsen, L.; Jaworska, J. Improving opportunities for regulatory acceptance of QSARs: The importance of model domain, uncertainty, validity and predictability. QSAR Comb. Sci., 2003, 22(3), 346-350.

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Year: 2019
Published on: 26 December, 2019
Page: [2643 - 2657]
Pages: 15
DOI: 10.2174/1568026619666191105111817
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