Arome, D, Chinedu, E. The importance of toxicity testing. J Pharm Bio Sci 2013, 4:146–148.

Parasuraman, S. Toxicological screening. J Pharmacol Pharmacother 2011, 2:74–79.

Auletta, AE, Dearfield, KL, Cimino, MC. Mutagenicity test schemes and guidelines: U.S. EPA office of pollution prevention and toxics and office of pesticide programs. Environ Mol Mutagen 1993, 21:38–45.

Rulis, AM, Hattan, DG. FDA`s priority‐based assessment of food additives: II general toxicity parameters. Regul Toxicol Pharmacol 1985, 5:152–174.

Shukla, SJ, Huang, R, Austin, CP, Xia, M. The future of toxicity testing: a focus on in vitro methods using a quantitative high‐throughput screening platform. Drug Discov Today 2010, 15:997–1007.

Raies, AB, Bajic, VB. In silico toxicology: computational methods for the prediction of chemical toxicity. WIREs Comput Mol Sci 2016, 6:147–172.

Bliss, CI. The method of PROBITS. Science 1934, 79:38–39.

Matthews, EJ, Kruhlak, NL, Cimino, MC, Benz, RD, Contrera, JF. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods. Regul Toxicol Pharmacol 2006, 44:97–110.

Zhang, M‐L, Zhou, Z‐H. A review on multi‐label learning algorithms. IEEE Trans Knowl Data Eng 2014, 26:1819–1837.

Lhasa Limited. Derek Nexus. 2017. Available at: https://www.lhasalimited.org/products/derek-nexus.htm. (Accessed September 9, 2017).

Patlewicz, G, Jeliazkova, N, Safford, R, Worth, A, Aleksiev, B. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res 2008, 19:495–524.

Ideaconsult Ltd. ToxTree. 2015. Available at: https://eurl‐ecvam.jrc.ec.europa.eu/laboratories‐research/predictive_toxicology/qsar_tools/toxtree. (Accessed September 9, 2017).

OECD. The OECD QSAR Toolbox. 2016. Available at: http://www.oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm. (Accessed August 15, 2016).

Matthews, EJ, Kruhlak, NL, Cimino, MC, Benz, RD, Contrera, JF. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul Toxicol Pharmacol 2006, 44:83–96.

Mishra, M, Fei, H, Huan, J. Computational prediction of toxicity. Int J Data Min Bioinform 2013, 8:338–348.

Jeliazkova, N, Jeliazkov, V. Hierarchical multi‐label classification of ToxCast datasets. In: *ToxCast Data Analysis Summit*. US EPA, Research Triangle Park, NC; 2009.

Mayr, A, Klambauer, G, Unterthiner, T, Hochreiter, S. DeepTox: toxicity prediction using deep learning. Front Environ Sci 2016, 3.

Eduati, F, Mangravite, LM, Wang, T, Tang, H, Bare, JC, Huang, R, Norman, T, Kellen, M, Menden, MP, Yang, J. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol 2015, 33:933–940.

Huang, R, Xia, M, Sakamuru, S, Zhao, J, Shahane, SA, Attene‐Ramos, M, Zhao, T, Austin, CP, Simeonov, A. Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat Commun 2016, 7:10425.

Jiang, Z, Xu, R, Dong, C. Identification of chemical toxicity using ontology information of chemicals. Comput Math Methods Med 2015, 2015:246374.

Chen, L, Lu, J, Zhang, J, Feng, K‐R, Zheng, M‐Y, Cai, Y‐D. Predicting chemical toxicity effects based on chemical‐chemical interactions. PLoS One 2013, 8:e56517.

Batke, M, Bitsch, A, Gundert‐Remy, U, Gütlein, M, Helma, C, Kramer, S, Maunz, A, Partosch, F, Seeland, M, Stahlmann, R. Multi‐label‐classification to predict repeated dose toxicity in the context of REACH. Naunyn Schmiedebergs Arch Pharmacol 2014, 387:S45.

Batke, M, Gütlein, M, Partosch, F, Gundert‐Remy, U, Helma, C, Kramer, S, Maunz, A, Seeland, M, Bitsch, A. Innovative strategies to develop chemical categories using a combination of structural and toxicological properties. Front Pharmacol 2016, 7:321.

Schafer, JL. Analysis of Incomplete Multivariate Data. Boca Raton, FL: CRC press; 1997.

Montanari, F, Zdrazil, B, Digles, D, Ecker, GF. Selectivity profiling of BCRP versus P‐gp inhibition: from automated collection of polypharmacology data to multi‐label learning. J Chem 2016, 8:7.

Kotsiantis, SB. Supervised machine learning: a review of classification techniques. Inf Dent 2007, 31:249–268.

Tsoumakas, G, Katakis, I. Multi‐label classification: an overview. Int J Data Wareh Min 2007, 3:1–13.

Luaces, O, Díez, J, Barranquero, J, Coz, JJ, Bahamonde, A. Binary relevance efficacy for multilabel classification. Prog Artif Intell 2012, 1:303–313.

Read, J, Pfahringer, B, Holmes, G, Frank, E. Classifier chains for multi‐label classification. Mach Learn 2011, 85:333–359.

Tsoumakas, G, Katakis, I, Vlahavas, I. Random k‐labelsets for multilabel classification. IEEE Trans Knowl Data Eng 2011, 23:1079–1089.

Blum, A, Mitchell, T. Combining labeled and unlabeled data with co‐training. In: Bartlett P, Mansour Y, eds. Proceedings of the Eleventh Annual Conference on Computational Learning Theory. Madison, WI: ACM; 1998, 92–100.

Wicker, J, Pfahringer, B, Kramer, S. Multi‐label classification using boolean matrix decomposition. In: *Proceedings of the 27th Annual ACM Symposium on Applied Computing 2012*, 2012, 179–186.

LeCun, Y, Bengio, Y, Hinton, G. Deep learning. Nature 2015, 521:436–444.

Clare, A, King, RD. Knowledge discovery in multi‐label phenotype data. In: De Raedt, L, Siebes, A, eds. Principles of Data Mining and Knowledge Discovery. Berlin and Heidelberg, Germany: Springer; 2001, 42–53.

Chiang, T‐H, Lo, H‐Y, Lin, S‐D. A ranking‐based KNN approach for multi‐label classification. In: Hoi SCH, Buntine W, eds. Proceedings of the Asian Conference on Machine Learning. PMLR: Singapore; 2012, 81–96.

Cover, T, Hart, P. Nearest neighbor pattern classification. IEEE Trans Inf Theory 1967, 13:21–27.

Podani, J. Distance, similarity. In: Introduction to the Exploration of Multivariate Biological Data. Leiden, The Netherlands: Backhuys Publishers; 2000, 55–110.

Cox, DR. The regression analysis of binary sequences. J R Stat Soc Series B Stat Methodol 1958, 20:215–242.

Ypma, TJ. Historical development of the Newton–Raphson method. SIAM Rev 1995, 37:531–551.

Nocedal, J. Updating quasi‐Newton matrices with limited storage. Math Comp 1980, 35:773–782.

Wright, SJ. Coordinate descent algorithms. Math Prog 2015, 151:3–34.

Schmidt, M, Le Roux, N, Bach, F. Minimizing finite sums with the stochastic average gradient. Math Prog 2017, 162:83–112.

Ng, AY. Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In: Greiner R, Schuumans D, eds. Proceedings of the twenty‐First International Conference on Machine Learning. Banff, Canada: ACM, 2004, 78–85.

Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ. Classification and Regression Trees. Monterey, CA: CRC; 1984.

Breiman, L. Random forests. Mach Learn 2001, 45:5–32.

Geurts, P, Ernst, D, Wehenkel, L. Extremely randomized trees. Mach Learn 2006, 63:3–42.

Boser, BE, Guyon, IM, Vapnik, VN. A training algorithm for optimal margin classifiers. In: Haussler D, ed. Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, PA: ACM; 1992, 144–152.

Burges, CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998, 2:121–167.

Crammer, K, Singer, Y. On the algorithmic implementation of multiclass kernel‐based vector machines. J Mach Learn Res 2002, 2:265–292.

Zhou, D, Bousquet, O, Lal, TN, Weston, J, Schölkopf, B. Learning with local and global consistency. In: *NIPS`03 Proceedings of the 16th International Conference on Neural Information Processing Systems*. Whistler, Canada, 2003.

Maron, ME, Kuhns, JL. On relevance, probabilistic indexing and information retrieval. J ACM 1960, 7:216–244.

Podani, J. Matrix Rearrangement. In: Introduction to the Exploration of Multivariate Biological Data. Leiden, The Netherlands: Backhuys Publishers; 2000, 285–311.

Powers, DMW. Evaluation: from precision, recall and f‐measure to ROC, informedness, markedness %26 correlation. J Mach Learn Tech 2011, 2:37.

Huang, Q, Tao, D, Li, X, Jin, L, Wei, G. Exploiting local coherent patterns for unsupervised feature ranking. IEEE Trans Syst Man Cybern B Cybern 2011, 41:1471–1482.

He, Y, Liew, CY, Sharma, N, Woo, SK, Chau, YT, Yap, CW. PaDEL‐DDPredictor: open‐source software for PD‐PK‐T prediction. J Comput Chem 2013, 34:604–610.

Ellison, CM, Sherhod, R, Cronin, MT, Enoch, SJ, Madden, JC, Judson, PN. Assessment of methods to define the applicability domain of structural alert models. J Chem Inf Model 2011, 51:975–985.

Jaworska, J, Nikolova‐Jeliazkova, N. How can structural similarity analysis help in category formation? SAR QSAR Environ Res 2007, 18:195–207.

Ideaconsult Ltd. Ambit discovery. 2006. Available at: http://ambit.sourceforge.net/download_ambitdiscovery.html. (Accessed September 19, 2017).

Dietterich, T. Overfitting and undercomputing in machine learning. ACM Comput Surv 1995, 27:326–327.

Willmott, CJ, Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 2005, 30:79–82.

Podani, J. Hierarchical clustering. In: Introduction to the Exploration of Multivariate Biological Data. Leiden, The Netherlands: Backhuys Publishers; 2000, 135–164.