Crowdsourced mapping of unexplored target space of kinase inhibitors


Cichonska A., Ravikumar B., Allaway R. J. , Wan F., Park S., Isayev O., ...More

NATURE COMMUNICATIONS, vol.12, no.1, 2021 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1038/s41467-021-23165-1
  • Journal Name: NATURE COMMUNICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus, BIOSIS, CAB Abstracts, Chemical Abstracts Core, EMBASE, Geobase, INSPEC, MEDLINE, Veterinary Science Database, Directory of Open Access Journals

Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.