Details

Autor(en) / Beteiligte

• Cichońska, 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

Titel

Crowdsourced mapping of unexplored target space of kinase inhibitors

Ist Teil von

• Nature communications, 2021-06, Vol.12 (1), p.3307-18, Article 3307

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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