Mapping the complete target space of drugs and drug-like compounds, including both intended ‘primary targets’ as well as secondary ‘off-targets’, is a critical part of drug discovery efforts. Such a map would enable one not only to explore the therapeutic potential of chemical agents but also to better predict and manage their possible adverse effects prior to clinical trials . However, the massive size of the chemical universe makes experimentally mapping the bioactivity of the full space of compound-target interactions quickly infeasible in practice, even with automated high-throughput profiling assays.
This IDG-DREAM Drug-Kinase Binding Prediction Challenge seeks to evaluate the power of statistical and machine learning models as a systematic and cost-effective means for catalyzing compound-target interaction mapping efforts by prioritizing most potent interactions for further experimental evaluation. The Challenge will focus on kinase inhibitors, due to their clinical importance , and will be implemented in a screening-based, pre-competitive drug discovery project in collaboration with theIlluminating the Druggable Genome (IDG) Kinase-focused Data and Resource Generation Center, consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, with the goal of extending the druggability of the human kinome space.
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Cichonska, Anna, Balaguru Ravikumar, Robert J. Allaway, Sungjoon Park, Fangping Wan, Olexandr Isayev, Shuya Li, et al. “Crowdsourced Mapping Extends the Target Space of Kinase Inhibitors.” BioRxiv, February 11, 2020, 2019.12.31.891812. https://doi.org/10.1101/2019.12.31.891812.