Advertisement

Computational Modeling of Nonlinear Phenomena Using Machine Learning

  • Anthony J. Hickey
  • Hugh D. C. Smyth
Chapter
  • 18 Downloads
Part of the AAPS Introductions in the Pharmaceutical Sciences book series (AAPSINSTR)

Abstract

Machine learning (ML) is a field of computer science that allows interrogation to allow modified navigation (learning) of the data and through statistical derivation prediction of unseen data or events. ML has been a high-profile topic for many years and is ubiquitous in many aspects of daily life – from e-mail spam and malware filtering to search results refining online customer service and fraud detection. More recently, ML has been pervasive in solving complex nonlinear phenomena in pharmaceutical and medical sciences. It has been used in modeling chemical data sets for two decades. It has only recently become a useful approach to improve healthcare diagnoses and to provide personalized medical treatments. The rapid growth in data collection and integration, as well as the accessibility of increasing computing power, especially in cloud services, explains this unforeseen capacity to transform data into information, information into knowledge, and knowledge into wisdom (see Fig. 7.1). In this section, we briefly introduce the concepts and types of ML and its application for drug discovery, drug product development, and clinical application. The literature in these fields and the importance and challenges of interpreting ML results are also discussed.

Keywords

Computational modeling Machine learning Artificial intelligence Drug discovery Product development Clinical application 

References

  1. Abuhammad, A., & Taha, M. O. (2016). QSAR studies in the discovery of novel type-II diabetic therapies. Expert Opinion on Drug Discovery, 11(2), 197–214.  https://doi.org/10.1517/17460441.2016.1118046CrossRefPubMedGoogle Scholar
  2. Alves, V., Braga, R., Muratov, E., & Andrade, C. (2018). Development of web and mobile applications for chemical toxicity prediction. Journal of the Brazilian Chemical Society, 29(5), 982–988.  https://doi.org/10.21577/0103-5053.20180013CrossRefGoogle Scholar
  3. Alves, V. M., Capuzzi, S. J., Braga, R. C., Borba, J. V. B., Silva, A. C., Luechtefeld, T., … Tropsha, A. (2018). A perspective and a new integrated computational strategy for skin sensitization assessment. ACS Sustainable Chemistry & Engineering, 6(3), 2845–2859.  https://doi.org/10.1021/acssuschemeng.7b04220CrossRefGoogle Scholar
  4. Alves, V. M., Golbraikh, A., Capuzzi, S. J., Liu, K., Lam, W. I., Korn, D. R., … Tropsha, A. (2018). Multi-Descriptor Read Across (MuDRA): A simple and transparent approach for developing accurate quantitative structure–activity relationship models. Journal of Chemical Information and Modeling, 58(6), 1214–1223.  https://doi.org/10.1021/acs.jcim.8b00124CrossRefPubMedGoogle Scholar
  5. Alves, V. M., Hwang, D., Muratov, E., Sokolsky-Papkov, M., Varlamova, E., Vinod, N., … Kabanov, A. (2019). Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. Science Advances, 5(6), eaav9784.  https://doi.org/10.1126/sciadv.aav9784CrossRefPubMedPubMedCentralGoogle Scholar
  6. Ashburn, T. T., & Thor, K. B. (2004). Drug repositioning: Identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673–683.  https://doi.org/10.1038/nrd1468CrossRefPubMedGoogle Scholar
  7. Bi, Y., Might, M., Vankayalapati, H., & Kuberan, B. (2017). Repurposing of Proton Pump Inhibitors as first identified small molecule inhibitors of endo-β-N-acetylglucosaminidase (ENGase) for the treatment of NGLY1 deficiency, a rare genetic disease. Bioorganic & Medicinal Chemistry Letters, 27(13), 2962–2966.  https://doi.org/10.1016/j.bmcl.2017.05.010CrossRefGoogle Scholar
  8. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., … Zieba, K. (2016). End to end learning for self-driving cars. ArXiv, 1604.07316. Retrieved from http://arxiv.org/abs/1604.07316
  9. Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010 (pp. 177–186).  https://doi.org/10.1007/978-3-7908-2604-3_16
  10. Braga, R. C., Alves, V. M., Muratov, E. N., Strickland, J., Kleinstreuer, N., Tropsha, A., & Andrade, C. H. (2017). Pred-skin: A fast and reliable web application to assess skin sensitization effect of chemicals. Journal of Chemical Information and Modeling, 57(5), 1013–1017.  https://doi.org/10.1021/acs.jcim.7b00194CrossRefPubMedGoogle Scholar
  11. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.  https://doi.org/10.1023/A:1010933404324CrossRefGoogle Scholar
  12. Capuzzi, S. J., Sun, W., Muratov, E. N., Martínez-Romero, C., He, S., Zhu, W., … Tropsha, A. (2018). Computer-aided discovery and characterization of novel Ebola virus inhibitors. Journal of Medicinal Chemistry, 61(8), 3582–3594.  https://doi.org/10.1021/acs.jmedchem.8b00035CrossRefPubMedPubMedCentralGoogle Scholar
  13. Casati, S., Aschberger, K., Barroso, J., Casey, W., Delgado, I., Kim, T. S., … Zuang, V. (2018). Standardisation of defined approaches for skin sensitisation testing to support regulatory use and international adoption: Position of the International Cooperation on Alternative Test Methods. Archives of Toxicology, 92(2), 611–617.  https://doi.org/10.1007/s00204-017-2097-4CrossRefPubMedGoogle Scholar
  14. Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538(7623), 20–23.  https://doi.org/10.1038/538020aCrossRefPubMedGoogle Scholar
  15. Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., … Gurram, P. (2017). Interpretability of deep learning models: A survey of results. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1–6).  https://doi.org/10.1109/UIC-ATC.2017.8397411
  16. Che, Z., Purushotham, S., Khemani, R., & Liu, Y. (2016). Interpretable deep models for ICU outcome prediction. In AMIA ... annual symposium proceedings. AMIA symposium, 2016 (pp. 371–380). Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/28269832
  17. Chen, J.-K., Shen, C.-R., & Liu, C.-L. (2010). N-acetylglucosamine: Production and applications. Marine Drugs, 8(9), 2493–2516.  https://doi.org/10.3390/md8092493CrossRefPubMedPubMedCentralGoogle Scholar
  18. Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., … Tropsha, A. (2014). QSAR modeling: Where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12), 4977–5010.  https://doi.org/10.1021/jm4004285CrossRefPubMedPubMedCentralGoogle Scholar
  19. Ciallella, H. L., & Zhu, H. (2019). Advancing computational toxicology in the big data era by artificial intelligence: Data-driven and mechanism-driven modeling for chemical toxicity. Chemical Research in Toxicology, 32(4), 536–547.  https://doi.org/10.1021/acs.chemrestox.8b00393CrossRefPubMedPubMedCentralGoogle Scholar
  20. Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., … Clozel, T. (2019). Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine, 25(10), 1519–1525.  https://doi.org/10.1038/s41591-019-0583-3CrossRefPubMedGoogle Scholar
  21. Dearden, J. C. (2016). The history and development of quantitative structure-activity relationships (QSARs). International Journal of Quantitative Structure-Property Relationships, 1(1), 1–44.  https://doi.org/10.4018/IJQSPR.2016010101CrossRefGoogle Scholar
  22. Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009). How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR and QSAR in Environmental Research, 20(3–4), 241–266.  https://doi.org/10.1080/10629360902949567CrossRefPubMedGoogle Scholar
  23. Dearden, J. C., Hewitt, M., Roberts, D. W., Enoch, S. J., Rowe, P. H., Przybylak, K. R., … Katritzky, A. R. (2015). Mechanism-based QSAR modeling of skin sensitization. Chemical Research in Toxicology, 28(10), 1975–1986.  https://doi.org/10.1021/acs.chemrestox.5b00197CrossRefPubMedGoogle Scholar
  24. Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J. C., Meyer, F., … Chabouis, A. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, 34(2), 196–203.  https://doi.org/10.1016/j.irbm.2013.01.010CrossRefGoogle Scholar
  25. Dhiman, K., & Agarwal, S. M. (2016). NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors. RSC Advances, 6(55), 49395–49400.  https://doi.org/10.1039/c6ra02772eCrossRefGoogle Scholar
  26. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. ArXiv, 1702.08608. Retrieved from http://arxiv.org/abs/1702.08608
  27. Dreyfus, H. (1979). What computers can’t do: The limits of artificial intelligence. London, UK: MIT Press.Google Scholar
  28. Durdagi, S., Erol, I., Dogan, B., & Berkay Sen, T. (2019). Integration of text mining and binary QSAR models for novel anti-hypertensive antagonist scaffolds. Biophysical Journal, 116(3), 478a.  https://doi.org/10.1016/j.bpj.2018.11.2583CrossRefGoogle Scholar
  29. Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., … Clark, A. M. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials, 18(5), 435–441.  https://doi.org/10.1038/s41563-019-0338-zCrossRefPubMedPubMedCentralGoogle Scholar
  30. Fernandez, M., Ban, F., Woo, G., Isaev, O., Perez, C., Fokin, V., … Cherkasov, A. (2019). Quantitative structure–price relationship (QS$R) Modeling and the development of economically feasible drug discovery projects. Journal of Chemical Information and Modeling, 59(4), 1306–1313.  https://doi.org/10.1021/acs.jcim.8b00747CrossRefPubMedGoogle Scholar
  31. Fourches, D., Muratov, E., & Tropsha, A. (2010). Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling, 50(7), 1189–1204.  https://doi.org/10.1021/ci100176xCrossRefPubMedPubMedCentralGoogle Scholar
  32. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., … Overington, J. P. (2012). ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(Database issue), D1100–D1107.  https://doi.org/10.1093/nar/gkr777CrossRefPubMedGoogle Scholar
  33. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Retrieved from http://www.deeplearningbook.org/
  34. Goto, T., Jo, T., Matsui, H., Fushimi, K., Hayashi, H., & Yasunaga, H. (2019). Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD: Journal of Chronic Obstructive Pulmonary Disease, 1–6.  https://doi.org/10.1080/15412555.2019.1688278
  35. Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116.  https://doi.org/10.1007/s11920-019-1094-0CrossRefPubMedGoogle Scholar
  36. Hisaki, T., Aiba, M., Yamaguchi, M., & Sasa, H. (2015). Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive , and developmental toxicities of cosmetic ingredients. The Journal of Toxicological Sciences, 40(2), 163–180.  https://doi.org/10.2131/jts.40.163CrossRefPubMedGoogle Scholar
  37. Horvitz, E. J., Apacible, J., Sarin, R., & Liao, L. (2012). Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service. ArXiv, 1207.1352. Retrieved from http://arxiv.org/abs/1207.1352
  38. Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., … Ng, A. Y. (2015). An empirical evaluation of deep learning on highway driving. ArXiv, 1504.01716. Retrieved from http://arxiv.org/abs/1504.01716
  39. Kepuska, V., & Bohouta, G. (2018). Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). In 2018 IEEE 8th annual computing and communication workshop and conference, CCWC 2018, 2018-January (pp. 99–103).  https://doi.org/10.1109/CCWC.2018.8301638
  40. Kerr, K. F., Bansal, A., & Pepe, M. S. (2012). Further insight into the incremental value of new markers: The interpretation of performance measures and the importance of clinical context. American Journal of Epidemiology, 176, 482–487.  https://doi.org/10.1093/aje/kws210CrossRefPubMedPubMedCentralGoogle Scholar
  41. Klein, R. J. (2005). Complement factor H polymorphism in age-related macular degeneration. Science (New York, N.Y.), 308(5720), 385–389.  https://doi.org/10.1126/science.1109557CrossRefGoogle Scholar
  42. Kleinstreuer, N. C., Karmaus, A. L., Mansouri, K., Allen, D. G., Fitzpatrick, J. M., & Patlewicz, G. (2018). Predictive models for acute oral systemic toxicity: A workshop to bridge the gap from research to regulation. Computational Toxicology, 8(4), 21–24.  https://doi.org/10.1016/j.comtox.2018.08.002CrossRefPubMedGoogle Scholar
  43. Koh, P. W., & Liang, P. (2017). Understanding black-box predictions via influence functions. In ICML’17 proceedings of the 34th international conference on machine learning (pp. 1885–1894). Retrieved from https://dl.acm.org/citation.cfm?id=3305576
  44. Lavecchia, A. (2015). Machine-learning approaches in drug discovery: Methods and applications. Drug Discovery Today, 20(3), 318–331.  https://doi.org/10.1016/j.drudis.2014.10.012CrossRefPubMedGoogle Scholar
  45. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.  https://doi.org/10.1038/nature14539CrossRefGoogle Scholar
  46. Lima, M. N. N., Melo-Filho, C. C., Cassiano, G. C., Neves, B. J., Alves, V. M., Braga, R. C., … Andrade, C. H. (2018). QSAR-driven design and discovery of novel compounds with antiplasmodial and transmission blocking activities. Frontiers in Pharmacology, 9, 146.  https://doi.org/10.3389/fphar.2018.00146CrossRefPubMedPubMedCentralGoogle Scholar
  47. Lipton, Z. C. (2016). The mythos of model interpretability. ArXiv, 1606.03490. Retrieved from http://arxiv.org/abs/1606.03490
  48. Liu, J., Mansouri, K., Judson, R. S., Martin, M. T., Hong, H., Chen, M., … Shah, I. (2015). Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chemical Research in Toxicology, 28, 738–751.  https://doi.org/10.1021/tx500501hCrossRefPubMedGoogle Scholar
  49. Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., … Tropsha, A. (2011). Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology, 24(8), 1251–1262.  https://doi.org/10.1021/tx200148aCrossRefPubMedPubMedCentralGoogle Scholar
  50. Low, Y. S., Alves, V. M., Fourches, D., Sedykh, A., Andrade, C. H., Muratov, E. N., … Tropsha, A. (2018). Chemistry-Wide Association Studies (CWAS): A novel framework for identifying and interpreting structure-activity relationships. Journal of Chemical Information and Modeling, 58(11), 2203–2213.  https://doi.org/10.1021/acs.jcim.8b00450CrossRefPubMedPubMedCentralGoogle Scholar
  51. Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470.  https://doi.org/10.1016/j.engappai.2016.12.002CrossRefGoogle Scholar
  52. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Retrieved December 4, 2019, from http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
  53. Melo Calixto, N., Braz dos Santos, D., Clecildo Barreto Bezerra, J., & de Almeida SilvaID, L. (2018). In silico repositioning of approved drugs against Schistosoma mansoni energy metabolism targets. PLoS One.  https://doi.org/10.1371/journal.pone.0203340
  54. Melo-Filho, C. C., Dantas, R. F., Braga, R. C., Neves, B. J., Senger, M. R., Valente, W. C. G., … Andrade, C. H. (2016). QSAR-driven discovery of novel chemical scaffolds active against Schistosoma mansoni. Journal of Chemical Information and Modeling, 56(7), 1357–1372.  https://doi.org/10.1021/acs.jcim.6b00055CrossRefPubMedPubMedCentralGoogle Scholar
  55. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.  https://doi.org/10.1093/bib/bbx044CrossRefPubMedCentralGoogle Scholar
  56. Mitchell, T. M. (1997). Machine learning. New York, NY: McGraw-Hill.Google Scholar
  57. Neves, B. J., Braga, R. C., Alves, V. M., Lima, M. N. N., Cassiano, G. C., Muratov, E. N., Costa, F.T.M., Andrade, C. H. (2019). Deep Learning-driven research for drug discovery: Tackling Malaria. PLOS Computational Biology, 16(2):e1007025, https://doi.org/10.1371/journal.pcbi.1007025
  58. Neves, B. J., Dantas, R. F., Senger, M. R., Melo-Filho, C. C., Valente, W. C. G., de Almeida, A. C. M., … Andrade, C. H. (2016). Discovery of new anti-schistosomal hits by integration of QSAR-based virtual screening and high content screening. Journal of Medicinal Chemistry, 59(15), 7075–7088.  https://doi.org/10.1021/acs.jmedchem.5b02038CrossRefPubMedPubMedCentralGoogle Scholar
  59. Nosengo, N. (2016). Can you teach old drugs new tricks? Nature, 534(7607), 314–316.  https://doi.org/10.1038/534314aCrossRefPubMedGoogle Scholar
  60. Pantaleao, S. Q., Fujii, D. G. V., Maltarollo, V. G., da C. Silva, D., Trossini, G. H. G., Weber, K. C., … Honorio, K. M. (2017). The role of QSAR and virtual screening studies in type 2 diabetes drug discovery. Medicinal Chemistry, 13(8), 706–720.  https://doi.org/10.2174/1573406413666170522152102CrossRefPubMedGoogle Scholar
  61. Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50.  https://doi.org/10.2308/ajpt-50009CrossRefGoogle Scholar
  62. Ping, P., Watson, K., Han, J., & Bui, A. (2017). Individualized knowledge graph: A viable informatics path to precision medicine. Circulation Research, 120(7), 1078–1080.  https://doi.org/10.1161/CIRCRESAHA.116.310024CrossRefPubMedPubMedCentralGoogle Scholar
  63. Polishchuk, P., Kuz’min, V., Artemenko, A., & Muratov, E. (2013). Universal approach for structural interpretation of QSAR/QSPR models. Molecular Informatics, 32, 843–853.CrossRefGoogle Scholar
  64. Renard, P., Alcolea, A., & Ginsbourger, D. (2013). Stochastic versus deterministic approaches. In J. Wainwright & M. Mulligan (Eds.), Environmental modelling: Finding simplicity in complexity (2nd ed.). Chichester, UK/Hoboken, NJ: Wiley.Google Scholar
  65. Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.  https://doi.org/10.1126/science.346.6213.1063CrossRefPubMedGoogle Scholar
  66. Speck-Planche, A. (2019). Multicellular target QSAR model for simultaneous prediction and design of anti-pancreatic cancer agents. ACS Omega, 4(2), 3122–3132.  https://doi.org/10.1021/acsomega.8b03693CrossRefGoogle Scholar
  67. Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Cherkasov, A., Li, J., … Tetko, I. V. (2010). Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling, 50(12), 2094–2111.  https://doi.org/10.1021/ci100253rCrossRefPubMedGoogle Scholar
  68. Tildesley, D., & Care, P. (2014). Press release: Next RSC president predicts that in 15 years no chemist will do bench experiments without computer-modelling them first. Retrieved from http://www.rsc.org/AboutUs/News/PressReleases/2013/Dominic-Tildesley-Royal-Society-of-Chemistry-President-Elect.asp
  69. Todeschini, R., & Consonni, V. (2009). Molecular descriptors for chemoinformatics (R. Mannhold, H. Kubinyi, & G. Folkers, Eds.).  https://doi.org/10.1002/9783527628766
  70. Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 29(6–7), 476–488.  https://doi.org/10.1002/minf.201000061CrossRefPubMedGoogle Scholar
  71. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., … Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.  https://doi.org/10.1038/s41573-019-0024-5CrossRefPubMedPubMedCentralGoogle Scholar
  72. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., Zhou, Z., … Bryant, S. H. (2012). PubChem’s BioAssay database. Nucleic Acids Research, 40(Database issue), D400–D412.  https://doi.org/10.1093/nar/gkr1132CrossRefPubMedGoogle Scholar
  73. Xu, C., Cheng, F., Chen, L., Du, Z., Li, W., Liu, G., … Tang, Y. (2012). In silico prediction of chemical Ames mutagenicity. Journal of Chemical Information and Modeling, 52(11), 2840–2847.  https://doi.org/10.1021/ci300400aCrossRefPubMedGoogle Scholar
  74. Zhang, L., Fourches, D., Sedykh, A., Zhu, H., Golbraikh, A., Ekins, S., … Tropsha, A. (2013). Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. Journal of Chemical Information and Modeling, 53(2), 475–492.  https://doi.org/10.1021/ci300421nCrossRefPubMedPubMedCentralGoogle Scholar
  75. Zhang, S., Wei, L., Bastow, K., Zheng, W., Brossi, A., Lee, K. H., & Tropsha, A. (2007). Antitumor agents 252. Application of validated QSAR models to database mining: Discovery of novel tylophorine derivatives as potential anticancer agents. Journal of Computer-Aided Molecular Design, 21(1–3), 97–112.  https://doi.org/10.1007/s10822-007-9102-6CrossRefPubMedPubMedCentralGoogle Scholar
  76. Zhao, K., & So, H.-C. (2019). Using drug expression profiles and machine learning approach for drug repurposing. Methods in Molecular Biology (Clifton, N.J.), 1903, 219–237.  https://doi.org/10.1007/978-1-4939-8955-3_13CrossRefGoogle Scholar
  77. Zhu, X., & Kruhlak, N. L. (2014). Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology, 321(1), 62–72.  https://doi.org/10.1016/j.tox.2014.03.009CrossRefPubMedGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2020

Authors and Affiliations

  • Anthony J. Hickey
    • 1
  • Hugh D. C. Smyth
    • 2
  1. 1.RTI InternationalResearch Triangle ParkUSA
  2. 2.College of PharmacyThe University of Texas at AustinAustinUSA

Personalised recommendations