Crowdsourced mapping of unexplored target space of kinase inhibitors. Academic Article uri icon


  • 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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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.

published proceedings

  • Nat Commun

altmetric score

  • 27.7

author list (cited authors)

  • Cichoska, A., Ravikumar, B., Allaway, R. J., Wan, F., Park, S., Isayev, O., ... Aittokallio, T.

citation count

  • 15

complete list of authors

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

publication date

  • January 2021