TY - JOUR

T1 - Crowdsourced mapping of unexplored target space of kinase inhibitors

AU - Cichonska, Anna

AU - Ravikumar, Balaguru

AU - Allaway, Robert J.

AU - Wan, Fangping

AU - Isayev, Olexandr

AU - Li, Shuya

AU - Mason, Michael

AU - Lamb, Andrew

AU - Tanoli, Ziaurrehman

AU - Jeon, Minji

AU - Kim, Sunkyu

AU - Popova, Mariya

AU - Capuzzi, Stephen

AU - Zeng, Jianyang

AU - Dang, Kristen

AU - Koytiger, Gregory

AU - Kang, Jaewoo

AU - Wells, Carrow I.

AU - Willson, Timothy M.

AU - Oprea, Tudor I.

AU - Schlessinger, Avner

AU - Drewry, David H.

AU - Stolovitzky, Gustavo

AU - Wennerberg, Krister

AU - Guinney, Justin

AU - Aittokallio, Tero

PY - 2021

Y1 - 2021

N2 - 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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data.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.

AB - 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.

KW - DRUG

KW - PHARMACOLOGY

KW - PREDICTION

KW - DISCOVERY

KW - PACKAGE

U2 - 10.1038/s41467-021-23165-1

DO - 10.1038/s41467-021-23165-1

M3 - Journal article

C2 - 34083538

VL - 12

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 3307

ER -