Crowdsourced mapping of unexplored target space of kinase inhibitors

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt


  • Anna Cichonska
  • Balaguru Ravikumar
  • Robert J. Allaway
  • Fangping Wan
  • Olexandr Isayev
  • Shuya Li
  • Michael Mason
  • Andrew Lamb
  • Ziaurrehman Tanoli
  • Minji Jeon
  • Sunkyu Kim
  • Mariya Popova
  • Stephen Capuzzi
  • Jianyang Zeng
  • Kristen Dang
  • Gregory Koytiger
  • Jaewoo Kang
  • Carrow I. Wells
  • Timothy M. Willson
  • Tudor I. Oprea
  • Avner Schlessinger
  • David H. Drewry
  • Gustavo Stolovitzky
  • Justin Guinney
  • Tero Aittokallio

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data.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.

TidsskriftNature Communications
Udgave nummer1
Antal sider18
StatusUdgivet - 2021

ID: 275331967