A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

Abstract

We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.

DOI: 10.1186/s13073-014-0057-7

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Cite this paper

@inproceedings{Jeon2014ASA, title={A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening}, author={Jouhyun Jeon and Satra Nim and Joan Teyra and Alessandro Datti and Jeffrey L. Wrana and Sachdev S. Sidhu and Jason Moffat and Philip M. Kim}, booktitle={Genome Medicine}, year={2014} }