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In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds ; Прогноз in silico токсикологических и фармакокинетических характеристик лекарственных соединений

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اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Contributors:
      The study was performed without external funding; Работа выполнена без спонсорской поддержки
    • بيانات النشر:
      Federal State Budgetary Institution ‘Scientific Centre for Expert Evaluation of Medicinal Products’ of the Ministry of Health of the Russian Federation (FSBI ‘SCEEMP’)
    • الموضوع:
      2023
    • Collection:
      Safety and Risk of Pharmacotherapy (E-Journal) / Безопасность и риск фармакотерапии
    • نبذة مختصرة :
      Scientific relevance. Studies of the toxicological and pharmacokinetic properties of medicinal compounds are a crucial stage of preclinical research; unsatisfactory results may invalidate further drug development. Therefore, the development of in silico methods for a preliminary pre-experimental assessment of toxicological and pharmacokinetic properties is a relevant and crucial task.Aim. The study aimed to review current approaches to in silico prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters of pharmacologically active compounds, in particular, the most important toxicological and pharmacokinetic parameters, and to present the results of the authors’ own research in this area.Discussion. According to the review of models for predicting the toxicological properties of chemical compounds (acute toxicity, carcinogenicity, mutagenicity, genotoxicity, endocrine toxicity, cytotoxicity, cardiotoxicity, hepatotoxicity, and immunotoxicity), the accuracy of predictions ranged from 74.0% to 98.0%. According to the review of models for predicting the pharmacokinetic properties of chemical compounds (gastrointestinal absorption; oral bioavailability; volume of distribution; total, renal, and hepatic clearance; and half-life), the coefficient of determination for the predictions ranged from 0.265 to 0.920. The literature review showed that the most widely used methods for in silico assessment of the ADMET parameters of pharmacologically active compounds included the random forest method and the support vector machines method. The authors compared the literature data with the results they obtained by modelling 12 toxicological and pharmacokinetic properties of chemical compounds using the consensus method in the IT Microcosm system and artificial neural networks. IT Microcosm outperformed the models described in the literature in terms of predicting 2 toxicological properties, including carcinogenicity and blood–brain barrier penetration (the prediction accuracy reached ...
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      https://www.risksafety.ru/jour/article/view/380/956; https://www.risksafety.ru/jour/article/view/380/959; https://www.risksafety.ru/jour/article/view/380/963; https://www.risksafety.ru/jour/article/view/380/975; https://www.risksafety.ru/jour/article/downloadSuppFile/380/369; https://www.risksafety.ru/jour/article/downloadSuppFile/380/370; https://www.risksafety.ru/jour/article/downloadSuppFile/380/441; Tsaioun K, Kates SA, eds. ADMET for Medicinal Chemists: A Practical Guide. New York: Wiley; 2011.; Дурнев АД. Лекарственная токсикология занимает важнейшее место в структуре доклинических исследований. Ведомости Научного центра экспертизы средств медицинского применения. Регуляторные исследования и экспертиза лекарственных средств. 2023;13(1):8–13.; Boroujerdi M. Pharmacokinetics and Toxicokinetics. New York: CRC Press; 2015.; Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137:104851. https://doi.org/10.1016/j.compbiomed.2021.104851; Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A guide to in silico drug design. Pharmaceutics. 2023;15(1):49. https://doi.org/10.3390/pharmaceutics15010049; Sammut C, Webb GI, eds. Encyclopedia of machine learning. New York: Springer; 2011.; Devillers J, Balaban AT, eds. Topological indices and related descriptors in QSAR and QSPR. New York: CRC Press; 2000.; Васильев ПМ, Спасов АА. Языки фрагментарного кодирования структуры соединений для компьютерного прогноза биологической активности. Российский химический журнал (Журнал Российского химического общества им. Д.И. Менделеева). 2006;50(2):108–27.; Engel T, Gasteiger J, eds. Chemoinformatics: Basic Concepts and Methods. Weinheim: Wiley-VCH; 2018.; Wang L, Ding J, Pan L, Cao D, Jiang H, Ding X. Quantum chemical descriptors in quantitative structure–activity relationship models and their applications. Chemometr Intell Lab Syst. 2021;217:104384. https://doi.org/10.1016/j.chemolab.2021.104384; Batool M, Ahmad B, Choi S. A structure-based drug discovery paradigm. Int J Mol Sci. 2019;20(11):2783. https://doi.org/10.3390/ijms20112783; Kar S, Leszczynski J. Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin Drug Discov. 2020;15(12):1473–87. https://doi.org/10.1080/17460441.2020.1798926; Fahrmeir L, Kneib T, Lang S, Marx BD. Regression: models, methods and applications. New York: Springer; 2021.; Gramatica P. Principles of QSAR modeling: comments and suggestions from personal experience. Int J Quant Struct Prop Relatsh. 2020;5(3):61–97. https://doi.org/10.4018/IJQSPR.20200701.oa1; Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12(85):2825–30.; Poroikov VV, Filimonov DA, Borodina YV, Lagunin AA, Kos A. Robustness of biological activity spectra predicting by computer program pass for noncongeneric sets of chemical compounds. J Med Chem. 2000;40(6):1349–55. https://doi.org/10.1021/ci000383k; Zhang Z. Introduction to machine learning: K-nearest neighbors. Ann Trans Med. 2016;4(11):218–24. https://doi.org/10.21037/atm.2016.03.37; Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.; Genuer R, Poggi J-M. Random forests with R (Use R!). New York: Springer; 2020.; Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat Commun. 2019;10(1):2674. https://doi.org/10.1038/s41467-019-09799-2; Aggarwal CC. Neural networks and deep learning: a textbook. New York: Springer; 2018.; Han S-H, Kim KW, Kim S, Youn YC. Artificial neural network: understanding the basic concepts without mathematics. Dement Neurocogn Disord. 2018;17(3):83–9. https://doi.org/10.12779/dnd.2018.17.3.83; Che J, Chen L, Guo ZH, Wang S, Aorigele С. Drug target group prediction with multiple drug networks. Comb Chem High Throughput Screen. 2020;23(4):274–84. https://doi.org/10.2174/1386207322666190702103927; Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20(18):4331. https://doi.org/10.3390/ijms20184331; Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys. 2016;18(18):12964–75. https://doi.org/10.1039/c6cp01555g; Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61. https://doi.org/10.1002/jcc.21334; Dickson CJ, Velez-Vega C, Duca JS. Revealing molecular determinants of hERG blocker and activator binding. J Chem Inf Model. 2020;60(1):192–203. https://doi.org/10.1021/acs.jcim.9b00773; Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem. 2011;32(4):742–55. https://doi.org/10.1002/jcc.21643; Joshi T, Sharma P, Joshi T, Pundir H, Mathpal S, Chandra S. Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19. Mol Divers. 2021;25(3):1665–77. https://doi.org/10.1007/s11030-020-10118-x; Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3–26. https://doi.org/10.1016/s0169-409x(00)00129-0; Oprea TI. Property distribution of drug-related chemical databases. J Comput Aided Mol Des. 2000;14(3):251–64. https://doi.org/10.1023/a:1008130001697; Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–23. https://doi.org/10.1021/jm020017n; Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1(1):55–68. https://doi.org/10.1021/cc9800071; Pardridge WM. CNS drug design based on principles of blood-brain barrier transport. J Neurochem. 1998;70(5):1781–92. https://doi.org/10.1046/j.1471-4159.1998.70051781.x; Bickerton GR, Paolini Gaia V, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4(2):90–8. https://doi.org/10.1038/nchem.1243; Ahlberg E, Carlsson L, Boyer S. Computational derivation of structural alerts from large toxicology data sets. J Chem Inf Model. 2014;54(10):2945–52. https://doi.org/10.1021/ci500314a; Daina A, Michielin O, Zoeteb V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. https://doi.org/10.1038/srep42717; O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform. 2011;3(1):33. https://doi.org/10.1186/1758-2946-3-33; Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–63. https://doi.org/10.1093/nar/gky318; Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58:4066–72. https://doi.org/10.1021/acs.jmedchem.5b00104; Dong J, Wang N-N, Yao Z-J, Zhang L, Cheng Y, Ouyang D, et al. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform. 2018;10(1):29. https://doi.org/10.1186/s13321-018-0283-x; Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2019;35(6):1067–69. https://doi.org/10.1093/bioinformatics/bty707; Lunghini F, Marcou G, Azam P, Horvath D, Patoux R, Van Miert E, et al. Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context. SAR QSAR Environ Res. 2019;30(12):879–97. https://doi.org/10.1080/1062936X.2019.1672089; Ruggiu F, Marcou G, Varnek A, Horvath D. ISIDA property-labelled fragment descriptors. Mol Inform. 2010;29(12):855–68. https://doi.org/10.1002/minf.201000099; Sosnin S, Karlov D, Tetko IV, Fedorov MV. Comparative study of multitask toxicity modeling on a broad chemical space. J Chem Inf Model. 2018;59(3):1062–72. https://doi.org/10.1021/acs.jcim.8b00685; Tice RR, Bassan A, Amberg A, Anger LT, Beal MA, Bellion P, et al. In silico approaches in carcinogenicity hazard assessment: current status and future needs. Comput Toxicol. 2021;20:100191. https://doi.org/10.1016/j.comtox.2021.100191; Zhong M, Nie X, Yan A, Yuan Q. Carcinogenicity prediction of noncongeneric chemicals by a support vector machine. Chem Res Toxicol. 2013;26(5):741–9. https://doi.org/10.1021/tx4000182; Sushko I, Novotarskyi S, Körner R, Pandey AK, Kovalishyn VV, Prokopenko VV, et al. Applicability domain for in silico models to achieve accuracy of experimental measurements. J Chemom. 2010;24(3–4):202–8. https://doi.org/10.1002/cem.1296; Xu C, Cheng F, Chen L, Du Z, Li W, Liu G, et al. In silico prediction of chemical Ames mutagenicity. J Chem Inf Model. 2012;52(11):2840–7. https://doi.org/10.1021/ci300400a; Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res. 2008;19(5–6):495–524. https://doi.org/10.1080/10629360802083871; Стрелкова ЮН. Понятия «генотоксичность» и «мутагенность». В кн. Advances in Science and Technology. Сборник статей XXVI международной научно-практической конференции. М.: Актуальность.РФ; 2020. Ч. I. С. 37–8.; Baderna D, Van Overmeire I, Lavado GJ, Gadaleta D, Mertens B. In silico Methods for chromosome damage. In: In silico methods for predicting drug toxicity. New York: Springer US; 2022. P. 185–200. https://doi.org/10.1007/978-1-0716-1960-5_8; Kusko R, Hong H. Machine learning and deep learning promote computational toxicology for risk assessment of chemicals. In: Machine learning and deep learning in computational toxicology. Cham: Springer International Publishing; 2023. P. 1–17. https://doi.org/10.1007/978-3-031-20730-3; Baderna D, Gadaleta D, Lostaglio E, Selvestrel G, Raitano G, Golbamaki A, et al. New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. J Hazard Mater. 2020;385:121638. https://doi.org/10.1016/j.jhazmat.2019.121638; Ferrari T, Cattaneo D, Gini G, Bakhtyari NG, Manganaro A, Benfenati E. Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction. SAR QSAR Environ Res. 2013;24(5):365–83. https://doi.org/10.1080/1062936x.2013.773376; Combarnous Y, Diep Nguyen TM. Comparative overview of the mechanisms of action of hormones and endocrine disruptor compounds. Toxics. 2019;7(1):5–14. https://doi.org/10.3390/toxics7010005; La Merrill MA, Vandenberg LN, Smith MT, Goodson W, Browne P, Patisaul HB, et al. Consensus on the key characteristics of endocrine-disrupting chemicals as a basis for hazard identification. Nat Rev Endocrinol. 2020;16(1):45–57. https://doi.org/10.1038/s41574-019-0273-8; Ruiz P, Sack A, Wampole M, Bobst S, Vracko M. Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere. 2021;178:99–109. https://doi.org/10.1016/j.chemosphere.2017.03.026; Matsuzaka Y, Uesawa Y. Molecular image-based prediction models of nuclear receptor agonists and antagonists using the deepsnap-deep learning approach with the Tox21 10K library. Molecules. 2020;25(12):2764. https://doi.org/10.3390/MOLECULES25122764; Collins SP, Barton-Maclaren TS. Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening. Front Toxicol. 2022;4:1–13. https://doi.org/10.3389/ftox.2022.981928; Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47(D1):D1102–D1109. https://doi.org/10.1093/nar/gky1033; Halle W, Halder M, Worth A, Genschow E. The registry of cytotoxicity: toxicity testing in cell cultures to predict acute toxicity (LD50) and to reduce testing in animals. Altern Lab Anim. 2003;31(2):89–198. https://doi.org/10.1177/026119290303100204; Petrescu AM, Paunescu V, Ilia G. The antiviral activity and cytotoxicity of 15 natural phenolic compounds with previously demonstrated antifungal activity. J Environ Sci Health B. 2019;54(6):498–504. https://doi.org/10.1080/03601234.2019.1574176; Chuipu C, Guo P, Zhou Y, Zhou J, Wang Q, Zhang F, et al. Deep learning-based prediction of drug-induced cardiotoxicity. J Chem Inf Model. 2019;59(3):1073–84. https://doi.org/10.1021/acs.jcim.8b00769; He S, Ye T, Wang R, Zhang C, Zhang X, Sun G, et al. An in s ilico model for predicting drug-induced hepatotoxicity. Int J Mol Sci. 2019;20(8):1897. https://doi.org/10.3390/ijms20081897; Ye H, Nelson LJ, Moral MGD, Martínez-Naves E, Cubero FJ. Dissecting the molecular pathophysiology of drug-induced liver injury. World J Gastroenterol. 2018;24(13):1373–85. https://doi.org/10.3748/wjg.v24.i13.1373; García-Cañaveras JC, Jiménez N, Gómez-Lechón MJ, Castell JV, Donato MT, Lahoz A. LC-MS untargeted metabolomic analysis of drug-induced hepatotoxicity in HepG2 cells. Electrophoresis. 2015;36(18):2294–302. https://doi.org/10.1002/elps.201500095; Kim E, Nam H. Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinformatics. 2017;18(Suppl 7):25–34. https://doi.org/10.1186/s12859-017-1638-4; Turabekova M, Rasulev B, Theodore M, Jackman J, Leszczynska D, Leszczynski J. Immunotoxicity of nanoparticles: a computational study suggests that CNTs and C60 fullerenes might be recognized as pathogens by Toll-like receptors. Nanoscale. 2014;6(7):3488–95. https://doi.org/10.1039/C3NR05772K; Roos C. Intestinal absorption of drugs: the impact of regional permeability, nanoparticles, and absorption-modifying excipients. Uppsala: Acta Universitatis Upsaliensis; 2018.; Venkatraman V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminform. 2021;13(1):75–86. https://doi.org/10.1186/s13321-021-00557-5; Wang N-N, Huang C, Dong J, Yao Z-J, Zhu M-F, Deng Z-K, et al. Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Adv. 2017;7(31):19007–18. https://doi.org/10.1039/C6RA28442F; Kumar R, Sharma A, Siddiqui MH, Tiwari RK. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr Drug Discov Technol. 2017;14(4):244–54. https://doi.org/10.2174/1570163814666170404160911; Миронов АН, ред. Руководство по проведению доклинических исследований лекарственных средств. Ч. 2. М.: Гриф и К; 2012.; Fagerholm U, Hellberg S, Spjuth O. Advances in predictions of oral bioavailability of candidate drugs in man with new machine learning methodology. Molecules. 2021;26(9):2572–82. https://doi.org/10.3390/molecules26092572; Ye Z, Yang Y, Li X, Cao D, Ouyang D. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Mol Pharm. 2019;16(2):533–41. https://doi.org/10.1021/acs.molpharmaceut.8b00816; Currie GM. Pharmacology, part 2: Introduction to pharmacokinetics. J Nucl Med Technol. 2018;46(3):221–30. https://doi.org/10.2967/jnmt.117.199638; Murad N, Pasikanti KK, Madej BD, Minnich A, McComas JM, Crouch S, et al. Predicting volume of distribution in humans: performance of in silico methods for a large set of structurally diverse clinical compounds. Drug Metab Dispos. 2021;49(2):169–78. https://doi.org/10.1124/dmd.120.000202; Lombardo F, Bentzien J, Berellini G, Muegge I. In silico models of human PK parameters. Prediction of volume of distribution using an extensive data set and a reduced number of parameters. J Pharm Sci. 2021;110(1):500–9. https://doi.org/10.1016/j.xphs.2020.08.023; Kosugi Y, Hosea N. Direct comparison of total clearance prediction: computational machine learning model versus bottom-up approach using in vitro assay. Mol Pharm. 2020;17(7):2299–309. https://doi.org/10.1021/acs.molpharmaceut.9b01294; Wang Y, Liu H, Fan Y, Chen X, Yang Y, Zhu L, et al. In silico prediction of human intravenous pharmacokinetic parameters with improved accuracy. J Chem Inf Model. 2019;59(9):3968–80. https://doi.org/10.1021/acs.jcim.9b00300; Chen J, Yang H, Zhu L, Wu Z, Li W, Tang Y, et al. In silico prediction of human renal clearance of compounds using quantitative structure-pharmacokinetic relationship models. Chem Res Toxicol. 2020;33(2):640–50. https://doi.org/10.1021/acs.chemrestox.9b00447; Watanabe R, Ohashi R, Esaki T, Kawashima H, Natsume-Kitatani Y, Nagao C, et al. Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Sci Rep. 2019;9(1):18782– 92. https://doi.org/10.1038/s41598-019-55325-1; Dawson DE, Ingle BL, Phillips KA, Nichols JW, Wambaugh JF, Tornero-Velez R. Designing QSARs for parameters of high-throughput toxicokinetic models using open-source descriptors. Environ Sci Technol. 2021;55(9):6505–17. https://doi.org/10.1021/acs.est.0c06117; Feinberg EN, Joshi E, Pande VS, Cheng AC. Improvement in ADMET prediction with multitask deep featurization. J Med Chem. 2020;63(16):8835–48. https://dx.doi.org/10.1021/acs.jmedchem.9b02187; Vassiliev PM, Spasov AA, Kosolapov VA, Kucheryavenko AF, Gurova NA, Anisimova VA. Consensus drug design using IT Microcosm. In: Gorb L, Kuz’min V, Muratov E, eds. Application of computational techniques in pharmacy and medicine; challenges and advances in computational chemistry and physics. Vol. 17. Springer, Dordrecht; 2014. P. 369–431. https://doi.org/10.1007/978-94-017-9257-8_12; Вао//doi.org/10.19163/1994-9480-2020-1(73)-31-33; Васильев ПМ, Спасов АА, Кочетков АН, Бабков ДА, Литвинов РА. Консенсусный прогноз in silico канцерогенной опасности мультитаргетных RAGE-ингибиторов. Волгоградский научно-медицинский журнал. 2020;(1):55–7.; Васильев ПМ, Спасов АА, Кочетков АН, Перфильев МА, Королева АР, Голубева АВ и др. Консенсусная оценка in silico общей безопасности мультитаргетных RAGE-ингибиторов. Волгоградский научно-медицинский журнал. 2020;(2):47–51.; Васильев ПМ, Спасов АА, Кочетков АН, Перфильев МА, Королева АР, Голубева АВ и др. Консенсусный прогноз in silico фармакокинетической предпочтительности мультитаргетных RAGE-ингибиторов. Вестник Волгоградского государственного медицинского университета. 2020;(2):100–4. https://doi.org/10.19163/1994-9480-2020-2(74)-100-104; Tomasulo P. ChemIDplus — super source for chemical and drug information. Med Ref Serv. 2002;21(1):53–9. https://doi.org/10.1300/j115v21n01_04; IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. Vol. 1–131. Lyon: IARC; 1972–2023.; Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, Felix E, et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019;47(D1):D930–40. https://doi.org/10.1093/nar/gky1075; Karthikeyan BS, Ravichandran J, Aparna SR, Samal A. DEDuCT 2.0: An updated knowledgebase and an exploration of the current regulations and guidelines from the perspective of endocrine disrupting chemicals. Chemosphere. 2021;267:128898. https://doi.org/10.1016/j.chemosphere.2020.128898; Wang Z, Yang H, Wu Z, Wang T, Li W, Tang Y, et al. In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods. Chem Med Chem. 2018;13(20):2189–201. https://doi.org/10.1002/cmdc.201800533; Колмогоров АН. О представлении непрерывных функций нескольких переменных в виде суперпозиции непрерывных функций одного переменного. Доклады АН СССР. 1958;114(5):953–6.; Shen J, Cheng F, Xu Y, Li W, Tang Y. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model. 2010;50(6):1034–41. https://doi.org/10.1021/ci100104j; Hilbe JM. Statistica 7: an overview. Am Stat. 2007;61(1): 91–4.; Li H, Ung CY, Yap CW, Xue Y, Li ZR, Cao ZW, et al. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem Res Toxicol. 2005;18(6):1071–80. https://doi.org/10.1021/tx049652h; Yan A, Wang Z, Cai Z. Prediction of human intestinal absorption by GA feature selection and support vector machine regression. Int J Mol Sci. 2008;9(10):1961–76. https://doi.org/10.3390/ijms9101961; https://www.risksafety.ru/jour/article/view/380
    • الرقم المعرف:
      10.30895/2312-7821-2023-11-4-390-408
    • الدخول الالكتروني :
      https://www.risksafety.ru/jour/article/view/380
      https://doi.org/10.30895/2312-7821-2023-11-4-390-408
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    • الرقم المعرف:
      edsbas.C392AB54