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

Genome Medicine
Jouhyun JeonPhilip M Kim

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.

Citations

Apr 26, 2015·Expert Opinion on Drug Discovery·Kobra Omidfar, Maryam Daneshpour
Dec 24, 2015·Frontiers in Physiology·Gaurav KandoiNey Lemke
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Related Concepts

Antineoplastic Agents
Breast
Classification
Genome
Learning
Ovarian Carcinoma
Peptides
Inhibitors
High Throughput Screening
Pharmacologic Substance

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