Crowdsourced mapping of unexplored target space of kinase inhibitors
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Crowdsourced mapping of unexplored target space of kinase inhibitors. / Cichonska, Anna; Ravikumar, Balaguru; Allaway, Robert J.; Wan, Fangping; 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.
I: Nature Communications, Bind 12, Nr. 1, 3307, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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 -
ID: 275331967