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

Xiong, Z. et al. (2021) Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge. PLoS Computational Biology, 17(9), e1009302. (doi: 10.1371/journal.pcbi.1009302) (PMID:34520464) (PMCID:PMC8483411)

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A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. 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, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited 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 rational polypharmacology.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Cagan, Professor Ross
Creator Roles:
Cagan, R.Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing
Authors: Xiong, Z., Jeon, M., Allaway, R. J., Kang, J., Park, D., Lee, J., Jeon, H., Ko, M., Jiang, H., Zheng, M., Tan, A. C., Guo, X., Dang, K. K., Tropsha, A., Hecht, C., Das, T. K., Carlson, H. A., Abagyan, R., Guinney, J., Schlessinger, A., and Cagan, R.
College/School:College of Medical Veterinary and Life Sciences > School of Cancer Sciences
Journal Name:PLoS Computational Biology
Publisher:Public Library of Science
ISSN (Online):1553-7358
Published Online:14 September 2021
Copyright Holders:Copyright © 2021 Xiong et al.
First Published:First published in PLoS Computational Biology 17(9): e1009302
Publisher Policy:Reproduced under a Creative Commons License

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