Registration for the IDG-DREAM Drug Kinase Binding Prediction Challenge is open!
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 the IDG Kinase-DRGC consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, toward extending the druggability of the human kinome space.
For further information, please see the IDG-DREAM Drug Kinase Binding Prediction Challenge website.
 Santos et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discovery 2017, 16, 19-34. Elkins et al. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat Biotech. 2016, 34, 95-103.