Molecular similarity-based predictions of the Tox21 screening outcome

Frontiers in Environmental Science

Volumn 3, Issue JUL, 2015, Pages

  Drwal, Malgorzata N ^a   Siramshetty, Vishal B ^a   Banerjee, Priyanka ^a,b   Goede, Andrean ^a   Preissner, Robert ^a,c   Dunkel, Mathias ^a  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

^c FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

Machine learning; Molecular fingerprints; Molecular similarity; Tox21 Data Challenge 2014; Toxicity prediction

Indexed keywords

EID: 85017158091     PISSN: None     EISSN: 2296665X     Source Type: Journal    
DOI: 10.3389/fenvs.2015.00054     Document Type: Article

References (31)

1. Attene-Ramos, M. S., Huang, R., Michael, S., Witt, K. L., Richard, A., Tice, R. R., et al. (2015). Profiling of the Tox21 chemical collection for mitochondrial function to identify compounds that acutely decrease mitochondrial membrane potential. Environ. Health Perspect. 123, 49-56. doi: 10.1289/ehp.1408642
2. Bajusz, D., Rácz, A., and Héberger, K. (2015). Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 7:20. doi: 10.1186/s13321-015-0069-3
3. Banerjee, P., Erehman, J., Gohlke, B. O., Wilhelm, T., Preissner, R., and Dunkel, M. (2015). Super Natural II-a database of natural products. Nucleic Acids Res. 43, D935-D939. doi: 10.1093/nar/gku886
4. Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., et al. (2014). The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, D1083-D1090. doi: 10.1093/nar/gkt1031
5. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J., and Bork, P. (2008). Drug target identification using side-effect similarity. Science 321, 263-266. doi: 10.1126/science.1158140
6. Chen, S., Zhou, D., Hsin, L. Y., Kanaya, N., Wong, C., Yip, R., et al. (2014). AroER tri-screen is a biologically relevant assay for endocrine disrupting chemicals modulating the activity of aromatase and/or the estrogen receptor. Toxicol. Sci. 139, 198-209. doi: 10.1093/toxsci/kfu023
7. Delfosse, V., Grimaldi, M., Le Maire, A., Bourguet, W., and Balaguer, P. (2014). Nuclear receptor profiling of bisphenol-A and its halogenated analogues. Vitam. Horm. 94, 229-251. doi: 10.1016/B978-0-12-800095-3.00009-2
8. Drwal, M. N., Banerjee, P., Dunkel, M., Wettig, M. R., and Preissner, R. (2014). ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res. 42, W53-W58. doi: 10.1093/nar/gku401
9. Duan, J., Dixon, S. L., Lowrie, J. F., and Sherman, W. (2010). Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model. 29, 157-170. doi: 10.1016/j.jmgm.2010.05.008
10. Ferdousy, E. Z., Islam, M., and Matin, M. (2013). Combination of naive bayes classifier and K-Nearest Neighbor (cNK) in the classification based predictive models. Comput. Inf. Sci. 6, 48. doi: 10.5539/cis.v6n3p48
11. Floris, M., Manganaro, A., Nicolotti, O., Medda, R., Mangiatordi, G. F., and Benfenati, E. (2014). A generalizable definition of chemical similarity for read-across. J. Cheminform. 6, 39. doi: 10.1186/s13321-014-0039-1
12. Gadaleta, D., Pizzo, F., Lombardo, A., Carotti, A., Escher, S. E., Nicolotti, O., et al. (2014). A k-NN algorithm for predicting the oral sub-chronic toxicity in the rat. ALTEX 31, 423-432. doi: 10.14573/altex.1405091s
13. Hall, L. H., and Kier, L. B. (1991). "The molecular connectivity chi indexes and kappa shape indexes in structure-property modeling," in Reviews in Computational Chemistry, eds K. B. Lipkowitz and D. B. Boyd (Hoboken, NJ: John Wiley & Sons, Inc.), 367-422.
14. Huang, R., Sakamuru, S., Martin, M. T., Reif, D. M., Judson, R. S., Houck, K. A., et al. (2014). Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci. Rep. 4:5664. doi: 10.1038/srep05664
15. Krewski, D., Acosta, D. Jr. Andersen, M., Anderson, H., Bailar, J. C. 3rd Boekelheide, K., et al. (2010). Toxicity testing in the 21st century: a vision and a strategy. J. Toxicol. Environ. Health B Crit. Rev. 13, 51-138. doi: 10.1080/10937404.2010.483176
16. Li, X., Chen, L., Cheng, F., Wu, Z., Bian, H., Xu, C., et al. (2014). In silico prediction of chemical acute oral toxicity using multi-classification methods. J. Chem. Inf. Model. 54, 1061-1069. doi: 10.1021/ci5000467
17. Liu, J., Mansouri, K., Judson, R. S., Martin, M. T., Hong, H., Chen, M., et al. (2015). predicting hepatotoxicity using toxcast in vitro bioactivity and chemical structure. Chem. Res. Toxicol. 28, 738-751. doi: 10.1021/tx500501h
18. Liu, X., Vogt, I., Haque, T., and Campillos, M. (2013). HitPick: a web server for hit identification and target prediction of chemical screenings. Bioinformatics 29, 1910-1912. doi: 10.1093/bioinformatics/btt303
19. Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., et al. (2012). Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361-367. doi: 10.1038/nature11159
20. Murray, I. A., Patterson, A. D., and Perdew, G. H. (2014). Aryl hydrocarbon receptor ligands in cancer: friend and foe. Nat. Rev. Cancer 14, 801-814. doi: 10.1038/nrc3846
21. Nadanaciva, S., and Will, Y. (2011). Investigating mitochondrial dysfunction to increase drug safety in the pharmaceutical industry. Curr. Drug Targets 12, 774-782. doi: 10.2174/138945011795528985
22. Nickel, J., Gohlke, B. O., Erehman, J., Banerjee, P., Rong, W. W., Goede, A., et al. (2014). SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 42, W26-W31. doi: 10.1093/nar/gku477
23. Nidhi, Glick, M., Davies, J. W., and Jenkins, J. L. (2006). Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J. Chem. Inf. Model. 46, 1124-1133. doi: 10.1021/ci060003g
24. Pasha, F. A., Neaz, M. M., Cho, S. J., Ansari, M., Mishra, S. K., and Tiwari, S. (2009). In silico quantitative structure-toxicity relationship study of aromatic nitro compounds. Chem. Biol. Drug Des. 73, 537-544. doi: 10.1111/j.1747-0285.2009.00799.x
25. Rogers, D., and Hahn, M. (2010). Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742-754. doi: 10.1021/ci100050t
26. Unterthiner, T., Mayr, A., Klambauer, G., and Hochreiter, S. (2015). Toxicity Prediction Using Deep Learning. Machine Learning. Available online at:http://arxiv.org/abs/1503.01445
27. Vogt, M., and Bajorath, J. (2008). Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints. Chem. Biol. Drug Des. 71, 8-14. doi: 10.1111/j.1747-0285.2007.00602.x
28. Weiss, B. (2012). The intersection of neurotoxicology and endocrine disruption. Neurotoxicology 33, 1410-1419. doi: 10.1016/j.neuro.2012.05.014
29. Xue, L., Godden, J. W., Stahura, F. L., and Bajorath, J. (2003). Design and evaluation of a molecular fingerprint involving the transformation of property descriptor values into a binary classification scheme. J. Chem. Inf. Comput. Sci. 43, 1151-1157. doi: 10.1021/ci030285+
30. Yang, C. H., Tarkhov, A., Marusczyk, J., Bienfait, B., Gasteiger, J., Kleinoeder, T., et al. (2015a). New publicly available chemical query language, csrml, to support chemotype representations for application to data mining and modeling. J. Chem. Inf. Model. 55, 510-528. doi: 10.1021/ci500667v
31. Yang, P., Yang, Y. H., Zhou, B. B., and Zomaya, A. Y. (2015b). A Review of Ensemble Methods in Bioinformatics. Curr. Bioinform. 5, 296-308. doi: 10.2174/157489310794072508