Crowdsourced mapping of unexplored target space of kinase inhibitors

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http://hdl.handle.net/10138/332475

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IDG-DREAM Drug-Kinase Binding , Cichonska , A , Ravikumar , B , Tanoli , Z & Aittokallio , T 2021 , ' Crowdsourced mapping of unexplored target space of kinase inhibitors ' , Nature Communications , vol. 12 , 3307 . https://doi.org/10.1038/s41467-021-23165-1

Title: Crowdsourced mapping of unexplored target space of kinase inhibitors
Author: IDG-DREAM Drug-Kinase Binding; Cichonska, Anna; Ravikumar, Balaguru; Tanoli, Ziaurrehman; Aittokallio, Tero
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Computational Systems Medicine
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2021-06-03
Language: eng
Number of pages: 18
Belongs to series: Nature Communications
ISSN: 2041-1723
URI: http://hdl.handle.net/10138/332475
Abstract: 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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data.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.
Subject: DRUG
PHARMACOLOGY
PREDICTION
DISCOVERY
PACKAGE
317 Pharmacy
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