Crowdsourced mapping of unexplored target space of kinase inhibitors

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2021-06-03
Department
Department of Computer Science
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Language
en
Pages
18
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Nature Communications, Volume 12, issue 1
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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data.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.
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DOI
10.1038/s41467-021-23165-1
Citation
Cichońska, A & IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium 2021, ' Crowdsourced mapping of unexplored target space of kinase inhibitors ', Nature Communications, vol. 12, no. 1, 3307 . https://doi.org/10.1038/s41467-021-23165-1
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https://urn.fi/URN:NBN:fi:aalto-202108048068
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[article-cris] Perustieteiden korkeakoulu / SCI