Machine learning-driven drug discovery
Drug discovery is difficult (Scannell et al., 2012), and especially so in cancer (Mullard, 2016). Experts have identified suboptimal preclinical strategies in cancer as a key problem (Hutchinson and Kirk, 2011). There exist clinically relevant functional assays (Friedman et al., 2015) with up to 87% clinical accuracy (Majumder et al., 2015) but these assays have limited throughput and are restricted to use as diagnostic assays instead of primary drug discovery tools. We hypothesize machine learning can select informative experiments to effectively utilize the low throughput of clinically relevant assays. Both we (Cobanoglu et al., 2013) and others (Murphy, 2011) have reported that active machine learning driven experimentation can increase efficiency in the drug discovery process in the preclinical stage. We have a view towards integrating our computational work with an experimental pipeline. That is exactly why we are housed in a biomedical powerhouse, the UT Southwestern Medical Center, to execute this vision.
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Friedman, A.A., Letai, A., Fisher, D.E., and Flaherty, K.T. (2015). Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756.
Majumder, B., Baraneedharan, U., Thiyagarajan, S., Radhakrishnan, P., Narasimhan, H., Dhandapani, M., Brijwani, N., Pinto, D.D., Prasath, A., Shanthappa, B.U., et al. (2015). Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat. Commun. 6, 6169.
Cobanoglu, M.C., Liu, C., Hu, F., Oltvai, Z.N., and Bahar, I. (2013). Predicting drug-target interactions using probabilistic matrix factorization. J. Chem. Inf. Model. 53, 3399–3409.
Murphy, R.F. (2011). An active role for machine learning in drug development. Nat. Chem. Biol. 7, 327–330.