Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU-based disease: the Multi-Targeting Drug DREAM Challenge

A predominant challenge in modern drug development is the identification of better therapeutics. Precision therapeutics, which have one molecular target, have been long-promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of polypharmacologic drugs, which can exhibit efficacious behavior caused by more than one biological target. Rational development of these molecules requires improved methods to predict chemical structures that specifically target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge where we asked participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a form of Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through polypharmacology.