Abstract
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both “handcrafted” and “data-driven,” are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen’s self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined.
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References
Barratt MD, Rodford RA (2001) The computational prediction of toxicity. Curr Opin Chem Biol 5:383–388
Kavlock RJ, Ankley G, Blancato J, Breen M, Conolly R, Dix D, Houck K, Hubal E, Judson R, Rabinowitz J, Richard A, Setzer RW, Shah I, Villeneuve D, Weber E (2008) Computational toxicology—a state of the science mini review. Toxicol Sci 103:14–27
Muster W, Breidenbach A, Fischer H, Kirchner S, Müller L, Pähler A (2008) Computational toxicology in drug development. Drug Discov Today 13:303–310
Valerio LG (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356–370
Nigsch F, Macaluso NJM, Mitchell JBO, Zmuidinavicius D (2009) Computational toxicology: an overview of the sources of data and of modelling methods. Expert Opin Drug Metab Toxicol 5:1–14
Merlot C (2010) Computational toxicology—a tool for early safety evaluation. Drug Discov Today 15:16–22
Raunio H (2011) In silico toxicology – non-testing methods. Front Pharmacol 2:33
Sun HM, Xia MH, Austin CP, Huang RL (2012) Paradigm shift in toxicity testing and modeling. AAPS J 14:473–480
Reisfeld B, Mayeno AN (2012) What is computational toxicology? In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol Volume I. Humana Press, Totowa, NJ, pp 3–7
Knudsen T, Martin M, Chandler K, Kleinstreuer N, Judson R, Sipes N (2013) Predictive models and computational toxicology. In: Barrow PC (ed) Teratogenicity testing: methods and protocols. Humana Press, Totowa, NJ, pp 343–374. https://doi.org/10.1007/978-1-62703-131-8_26
Ekins S (2014) Progress in computational toxicology. J Pharmacol Toxicol Methods 69:115–140
Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Mod 52:1413–1437
Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2015) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010
Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. In: Methods and principles in medicinal chemistry, vol 41. Wiley-VCH, Weinheim
Baskin I, Varnek A (2008) Fragment descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening. In: Varnek A, Tropsha A (eds) Chemoinformatics approaches to virtual screening. RSC Publisher, Cambridge, pp 1–43
Baskin I, Varnek A (2008) Building a chemical space based on fragment descriptors. Comb Chem High Throughput Screen 11:661–668
Varnek A, Fourches D, Hoonakker F, Solov’ev V (2005) Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J Comput Aided Mol Des 19:693–703
Marcou G, Horvath D, Solov'ev V, Arrault A, Vayer P, Varnek A (2012) Interpretability of SAR/QSAR models of any complexity by atomic contributions. Mol Inform 31:639–642
Draper NR, Smith H (1998) Applied regression analysis, 3rd edn. John Wiley, New York
Lyubimova IK, Abilev SK, Gal'berstam NM, Baskin II, Palyulin VA, Zefirov NS (2001) Computer-aided prediction of the mutagenic activity of substituted polycyclic compounds. Biol Bull 28:139–145
Enslein K, Gombar VK, Blake BW (1994) Use of SAR in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program. Mutat Res 305:47–61
Klopman G (1984) Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules. J Am Chem Soc 106:7315–7321
Rosenkranz HS, Klopman G (1988) CASE, the computer-automated structure evaluation system, as an alternative to extensive animal testing. Toxicol Ind Health 4:533–540
Klopman G (1992) MULTICASE. 1. A hierarchical computer automated structure evaluation program. Quant Struct-Act Relat 11(2):176–184. https://doi.org/10.1002/qsar.19920110208
Klopman G (1998) The MultiCASE program II. Baseline activity identification algorithm (BAIA). J Chem Inf Comput Sci 38:78–81
Klopman G (1996) The META-CASETOX system. In: Puijnenburg WJGM, Damborsky J (eds) Biodegradability prediction. Springer, Berlin, pp 27–40
Matthews EJ, Contrera JF (1998) A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul Toxicol Pharmacol 28:242–264
Klopman G, Chakravarti SK, Harris N, Ivanov J, Saiakhov RD (2003) In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system. SAR QSAR Environ Res 14:165–180
Klopman G, Chakravarti SK, Zhu H, Ivanov JM, Saiakhov RD (2004) ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. J Chem Inf Comput Sci 44:704–715
Klopman G, Ivanov J, Saiakhov R, Chakravarti S (2005) MC4PC–an artificial intelligence approach to the discovery of structure toxic activity relationships (STAR). In: Helma C (ed) Predictive toxicology. CRC Press, Boca Raton, pp 423–457
Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 2:64–73
Xiao Y, Qiao Y, Zhang J, Lin S, Zhang W (1997) A method for substructure search by atom-centered multilayer code. J Chem Inf Comput Sci 37:701–704
Glen RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9:199–204
Filimonov D, Poroikov V, Borodina Y, Gloriozova T (1999) Chemical similarity assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J Chem Inf Comput Sci 39:666–670
Hassan M, Brown RD, Varma-O'Brien S, Rogers D (2006) Cheminformatics analysis and learning in a data pipelining environment. Mol Divers 10(3):283–299
Metz JT, Huth JR, Hajduk PJ (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des 21:139–144
Langdon SR, Mulgrew J, Paolini GV, van Hoorn WP (2010) Predicting cytotoxicity from heterogeneous data sources with Bayesian learning. J Cheminform 2:11
Xia X, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47:4463–4470
Liew CY, Lim YC, Yap CW (2011) Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des 25:855
Poroikov VV, Filimonov DA, Borodina YV, Lagunin AA, Kos A (2000) Robustness of biological activity spectra predicting by computer program PASS for noncongeneric sets of chemical compounds. J Chem Inf Comput Sci 4:1349–1355
Lagunin AA, Dearden JC, Filimonov DA, Poroikov VV (2005) Computer-aided rodent carcinogenicity prediction. Mutat Res 586:138–146
Borodina Y, Sadym A, Filimonov D, Blinova V, Dmitriev A, Poroikov V (2003) Predicting biotransformation potential from molecular structure. J Chem Inf Comput Sci 43:1636–1646
Borodina Y, Rudik A, Filimonov D, Kharchevnikova N, Dmitriev A, Blinova V, Poroikov V (2004) A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches. J Chem Inf Comput Sci 44:1998–2009
Rudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV (2014) Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm. J Chem Inf Mod 54:498–507
Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V (2015) SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics 31:2046–2048
Rudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV (2016) Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics. J Cheminf 8:68
Rudik AV, Bezhentsev VM, Dmitriev AV, Druzhilovskiy DS, Lagunin AA, Filimonov DA, Poroikov VV (2017) MetaTox: web application for predicting structure and toxicity of xenobiotics’ metabolites. J Chem Inf Mod 57:638–642
Saigo H, Tsuda K (2010) Graph mining in chemoinformatics. In: Lodhi H, Yamanishi Y (eds) Chemoinformatics and advanced machine learning perspectives: complex computational methods and collaborative techniques. IGI Global, Hershey, PA, pp 95–128
Saigo H, Kadowaki T, Tsuda K (2006) A linear programming approach for molecular QSAR analysis. Paper presented at the International Workshop on Mining and Learning with Graphs 2006, Berlin
Zheng W, Tropsha A (2000) Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci 40:185–194
Rodgers AD, Zhu H, Fourches D, Rusyn I, Tropsha A (2010) Modeling liver-related adverse effects of drugs using k nearest neighbor quantitative structure−activity relationship method. Chem Res Toxicol 23:724–732
Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York
Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Czermiński R, Yasri A, Hartsough D (2001) Use of support vector machine in pattern classification: application to QSAR studies. Mol Inform 20:227–240
Khandelwal A, Krasowski MD, Reschly EJ, Sinz MW, Swaan PW, Ekins S (2008) Machine learning methods and docking for predicting human pregnane X receptor activation. Chem Res Toxicol 21:1457–1467
Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A (2010) Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chem Res Toxicol 23:171–183
Artemenko NV, Baskin II, Palyulin VA, Zefirov NS (2001) Prediction of physical properties of organic compounds using artificial neural networks within the substructure approach. Dokl Chem 381:317–320
Artemenko NV, Baskin II, Palyulin VA, Zefirov NS (2003) Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds. Russ Chem Bull 52:20–29
Zhokhova NI, Baskin II, Palyulin VA, Zefirov AN, Zefirov NS (2007) Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies. Dokl Chem 417:282–284
Sushko I, Novotarskyi S, Korner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Muller KR, Xi L, Liu H, Yao X, Oberg T, Hormozdiari F, Dao P, Sahinalp C, Todeschini R, Polishchuk P, Artemenko A, Kuz'min V, Martin TM, Young DM, Fourches D, Muratov E, Tropsha A, Baskin I, Horvath D, Marcou G, Muller C, Varnek A, Prokopenko VV, Tetko IV (2010) Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. J Chem Inf Model 50:2094–2111
Ralaivola L, Swamidass SJ, Saigo H, Baldi P (2005) Graph kernels for chemical informatics. Neural Netw 18:1093–1110
Rupp M, Schneider G (2010) Graph kernels for molecular similarity. Mol Inform 29:266–273
Kashima H, Tsuda K, Inokuchi A (2003) Marginalized kernels between labeled graphs. In: Proceedings, twentieth international conference on machine learning, vol 1. AAAI Press, Washington D.C., pp 321–328
Menchetti S, Costa F, Frasconi P 2005 Weighted decomposition kernels. In: Proceedings of the 22nd international conference on Machine learning. ACM, pp. 585–592
Swamidass SJ, Chen J, Phung P, Ralaivola L, Baldi P (2005) Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Bioinformatics 21:I359–I368
Mahé P, Ueda N, Akutsu T, Perret J-L, Vert J-P (2005) Graph kernels for molecular structure-activity relationship analysis with support vector machines. J Chem Inf Mod 45:939–951
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman & Hall/CRC, Wadsworth, California
Cheng A, Dixon SL (2003) In silico models for the prediction of dose-dependent human hepatotoxicity. J Comput Aided Mol Des 17:811–823
Susnow RG, Dixon SL (2003) Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J Chem Inf Comput Sci 43:1308–1315
Feng J, Lurati L, Ouyang H, Robinson T, Wang Y, Yuan S, Young SS (2003) Predictive toxicology: benchmarking molecular descriptors and statistical methods. J Chem Inf Comput Sci 43:1463–1470
Cramer GM, Ford RA, Hall RL (1976) Estimation of toxic hazard—a decision tree approach. Food Cosmet Toxicol 16:255–276
Verhaar HJM, van Leeuwen CJ, Hermens JLM (1992) Classifying environmental pollutants. Chemosphere 25:471–491
Walker JD, Gerner I, Hulzebos E, Schlegel K (2005) The skin irritation corrosion rules estimation tool (SICRET). QSAR Comb Sci 24:378–384
Gerner I, Liebsch M, Spielmann H (2005) Assessment of the eye irritating properties of chemicals by applying alternatives to the Draize rabbit eye test: the use of QSARs and in vitro tests for the classification of eye irritation. Altern Lab Anim 33:215–237
Benigni R, Bossa C (2008) Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. Toxicol Mech Methods 18:137–147
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional, New York
DeLisle RK, Dixon SL (2004) Induction of decision trees via evolutionary programming. J Chem Inf Comput Sci 44:862–870
Dietterichl TG (2002) Ensemble learning. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 405–408
Svetnik V, Wang T, Tong C, Liaw A, Sheridan RP, Song Q (2005) Boosting: an ensemble learning tool for compound classification and QSAR modeling. J Chem Inf Mod 45:786–799
Baskin II, Marcou G, Horvath D, Varnek A (2017) Bagging and boosting of classification models. In: Tutorials in chemoinformatics. John Wiley & Sons, Ltd, Hoboken, pp 241–247
Baskin II, Marcou G, Horvath D, Varnek A (2017) Bagging and boosting of regression models. In: Tutorials in chemoinformatics. John Wiley & Sons, Ltd, Hoboken, pp 249–255
Baskin II, Marcou G, Horvath D, Varnek A (2017) Random subspaces and random forest. In: Tutorials in chemoinformatics. John Wiley & Sons, Ltd, Hoboken, pp 263–269
Baskin II, Marcou G, Horvath D, Varnek A (2017) Stacking. In: Tutorials in chemoinformatics. John Wiley & Sons, Ltd, Hoboken, pp 271–278
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal 20:832–844
Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378
Breiman L (1996) Stacked regressions. Mach Learn 24:49–64
Breiman L (2001) Random forests. Mach Learn 45:5–32
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958
Li S, Fedorowicz A, Singh H, Soderholm SC (2005) Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Mod 45:952–964
Zhang Q-Y, Aires-de-Sousa J (2007) Random forest prediction of mutagenicity from empirical physicochemical descriptors. J Chem Inf Mod 47:1–8
Polishchuk PG, Muratov EN, Artemenko AG, Kolumbin OG, Muratov NN, Kuz'min VE (2009) Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 49:2481–2488
Vasanthanathan P, Taboureau O, Oostenbrink C, Vermeulen NPE, Olsen L, Jorgensen FS (2009) Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Drug Metab Dispos 37:658–664
Rumelhart DE, McClelland JL (1986) Parallel distributed processing, vol 1,2. MIT Press, Cambridge, MA
Gasteiger J, Zupan J (1993) Neural networks in chemistry. Angew Chem Int Ed Engl 105:503–527
Halberstam NM, Baskin II, Palyulin VA, Zefirov NS (2003) Neural networks as a method for elucidating structure-property relationships for organic compounds. Russ Chem Rev 72:629–649
Baskin II, Palyulin VA, Zefirov NS (2008) Neural networks in building QSAR models. Methods Mol Biol 458:137–158
Baskin II, Winkler D, Tetko IV (2016) A renaissance of neural networks in drug discovery. Expert Opin Drug Discovery 11:785–795
Villemin D, Cherqaoui D, Mesbah A (1994) Predicting carcinogenicity of polycyclic aromatic hydrocarbons from back-propagation neural network. J Chem Inf Comput Sci 34:1288–1293
Xu L, Ball JW, Dixon SL, Jurs PC (1994) Quantitative structure-activity relationships for toxicity of phenols using regression analysis and computational neural networks. Environ Toxicol Chem 13:841–851
Devillers J, Bintein S, Domine D, Karcher W (1995) A general QSAR model for predicting the toxicity of organic chemicals to luminescent bacteria (Microtox test). SAR QSAR Environ Res 4:29–38
Molnar L, Keseru GM, Papp A, Lorincz Z, Ambrus G, Darvas F (2006) A neural network based classification scheme for cytotoxicity predictions: validation on 30,000 compounds. Bioorg Med Chem Lett 16(4):1037–1039
Hatrik S, Zahradnik P (1996) Neural network approach to the prediction of the toxicity of benzothiazolium salts from molecular structure. J Chem Inf Comput Sci 36:992–995
Zakarya D, Larfaoui EM, Boulaamail A, Lakhlifi T (1996) Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network. SAR QSAR Environ Res 5:269–279
Eldred DV, Jurs PC (1999) Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure. SAR QSAR Environ Res 10:75–99
Devillers J, Flatin J (2000) A general QSAR model for predicting the acute toxicity of pesticides to Oncorhynchus mykiss. SAR QSAR Environ Res 1:25–43
Devillers J (2001) A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus. SAR QSAR Environ Res 11:397–417
Devillers J, Pham-Delegue MH, Decourtye A, Budzinski H, Cluzeau S, Maurin G (2002) Structure-toxicity modeling of pesticides to honey bees. SAR QSAR Environ Res 13:641–648
Kaiser KLE (2003) The use of neural networks in QSARs for acute aquatic toxicological endpoints. J Mol Struct (THEOCHEM) 622:85–95
Zakarya D, Boulaamail A, Larfaoui EM, Lakhlifi T (1997) QSARs for toxicity of DDT-type analogs using neural network. SAR QSAR Environ Res 6:183–203
Eldred DV, Weikel CL, Jurs PC, Kaiser KLE (1999) Prediction of fathead minnow acute toxicity of organic compounds from molecular structure. Chem Res Toxicol 12:670–678
Martin TM, Young DM (2001) Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method. Chem Res Toxicol 14:1378–1385
Moore DRJ, Breton RL, MacDonald DB (2003) A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish. Environ Toxicol Chem 22:1799–1809
Garg A, Bhat KL, Bock CW (2002) Mutagenicity of aminoazobenzene dyes and related structures: a QSAR/QPAR investigation. Dyes Pigments 55:35–52
Shoji R (2005) The potential performance of artificial neural networks in QSTRs for predicting ecotoxicity of environmental pollutants. Curr Comput Aided Drug Des 1:65–72
Dearden JC, Rowe PH (2015) Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation. Methods Mol Biol 1260:65–88
Tetko IV, Livingstone DJ, Luik AI (1995) Neural network studies. 1. Comparison of overfitting and overtraining. J Chem Inf Comput Sci 35:826–833
Tikhonov AN, Arsenin VA (1977) Solution of ill-posed problems. Winston & Sons, Washington
Winkler DA, Burden FR (2004) Bayesian neural nets for modeling in drug discovery. Drug Discov Today: BIOSILICO 2:104–111
Burden F, Winkler D (2008) Bayesian regularization of neural networks. Methods Mol Biol 458:25–44
Burden FR, Ford MG, Whitley DC, Winkler DA (2000) Use of automatic relevance determination in QSAR studies using Bayesian neural networks. J Chem Inf Comput Sci 40:1423–1430
Burden FR, Winkler DA (2000) A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem Res Toxicol 13:436–440
Cronin MTD, Schultz TW (2001) Development of quantitative structure-activity relationships for the toxicity of aromatic compounds to tetrahymena pyriformis: comparative assessment of the methodologies. Chem Res Toxicol 14:1284–1295
Polley MJ, Burden FR, Winkler DA (2005) Predictive human intestinal absorption QSAR models using Bayesian regularized neural networks. Aust J Chem 58:859–863
Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA (2012) Modeling biological activities of nanoparticles. Nano Lett 12:5808–5812
Tetko IV (2002) Neural network studies. 4. Introduction to associative neural networks. J Chem Inf Comput Sci 42:717–728
Novotarskyi S, Abdelaziz A, Sushko Y, Körner R, Vogt J, Tetko IV (2016) ToxCast EPA in vitro to in vivo challenge: insight into the rank-I model. Chem Res Toxicol 29:768–775
Abdelaziz A, Spahn-Langguth H, Schramm K-W, Tetko IV (2016) Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge. Front Environ Sci 4. https://doi.org/10.3389/fenvs.2016.00002
Sushko I, Novotarskyi S, Körner R, Pandey AK, Rupp M, Teetz W, Brandmaier S, Abdelaziz A, Prokopenko VV, Tanchuk VY, Todeschini R, Varnek A, Marcou G, Ertl P, Potemkin V, Grishina M, Gasteiger J, Schwab C, Baskin II, Palyulin VA, Radchenko EV, Welsh WJ, Kholodovych V, Chekmarev D, Cherkasov A, Aires-De-Sousa J, Zhang QY, Bender A, Nigsch F, Patiny L, Williams A, Tkachenko V, Tetko IV (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput Aided Mol Des 25:533–554
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127
Gawehn E, Hiss JA, Schneider G (2016) Deep learning in drug discovery. Mol Inform 35:3–14
Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comp Chem 38:1291–1307
Ekins S (2016) The next era: deep learning in pharmaceutical research. Pharm Res 33:2594–2603
Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) DeepTox: toxicity prediction using deep learning. Front Environ Sci 3:80
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. Pattern Anal Mach Intell IEEE Trans 35:1798–1828
Kohonen T (2001) Self-organizing maps. Springer, Berlin Heidelberg
Anzali S, Barnickel G, Krug M, Sadowski J, Wagener M, Gasteiger J, Polanski J (1996) The comparison of geometric and electronic properties of molecular surfaces by neural networks: application to the analysis of corticosteroid-binding globulin activity of steroids. J Comput Aided Mol Des 10:521–534
Hecht-Nielsen R (1987) Counterpropagation networks. Appl Opt 26:4979–4984
Vracko M (1997) A study of structure-carcinogenic potency relationship with artificial neural networks. The using of descriptors related to geometrical and electronic structures. J Chem Inf Comput Sci 37:1037–1043
Mazzatorta P, Vracko M, Jezierska A, Benfenati E (2003) Modeling toxicity by using supervised Kohonen neural networks. J Chem Inf Comput Sci 43:485–492
Spycher S, Pellegrini E, Gasteiger J (2005) Use of structure descriptors to discriminate between modes of toxic action of phenols. J Chem Inf Model 45:200–208
Bishop CM, Svensén M, Williams CKI (1998) GTM: the generative topographic mapping. Neural Comput 10:215–234
Kireeva N, Baskin II, Gaspar HA, Horvath D, Marcou G, Varnek A (2012) Generative topographic mapping (GTM): universal tool for data visualization, structure-activity modeling and dataset comparison. Mol Inform 31:301–312
Gaspar HA, Baskin II, Marcou G, Horvath D, Varnek A (2015) Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. J Chem Inf Mod 55:84–94
Gaspar HA, Baskin II, Marcou G, Horvath D, Varnek A (2015) GTM-based QSAR models and their applicability domains. Mol Inform 34:348–356
Gaspar HA, Baskin II, Marcou G, Horvath D, Varnek A (2015) Stargate GTM: bridging descriptor and activity spaces. J Chem Inf Model 55:2403–2410
Gaspar HA, Baskin II, Varnek A (2016) Visualization of a multidimensional descriptor space. In: Frontiers in molecular design and chemical information science–Herman Skolnik Award Symposium 2015: Jürgen Bajorath, vol 1222. ACS Symposium Series, vol 1222. American Chemical Society, pp. 243–267
Gaspar HA, Sidorov P, Horvath D, Baskin II, Marcou G, Varnek A (2016) Generative topographic mapping approach to chemical space analysis. In: Frontiers in molecular design and chemical information science–Herman Skolnik Award Symposium 2015: Jürgen Bajorath, vol 1222. ACS Symposium Series, vol 1222. American Chemical Society, pp. 211–241
Kireeva N, Kuznetsov SL, Bykov AA, Tsivadze AY (2012) Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models. SAR QSAR Environ Res 24:103–117
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Baskin, I.I. (2018). Machine Learning Methods in Computational Toxicology. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_5
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