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  Crowdsourced mapping of unexplored target space of kinase inhibitors

Cichońska, A., Ravikumar, B., Allaway, R. J., Wan, F., Park, S., Isayev, O., et al. (2021). Crowdsourced mapping of unexplored target space of kinase inhibitors. Nature Communications, 12: 3307. doi:10.1038/s41467-021-23165-1.

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Cichońska, Anna , Author
Ravikumar, Balaguru , Author
Allaway, Robert J. , Author
Wan, Fangping, Author
Park, Sungjoon, Author
Isayev, Olexandr , Author
Li, Shuya, Author
Mason, Michael, Author
Lamb, Andrew, Author
Tanoli, Ziaurrehman , Author
Jeon, Minji , Author
Kim, Sunkyu, Author
Popova, Mariya , Author
Capuzzi, Stephen , Author
Zeng, Jianyang, Author
Dang, Kristen, Author
Koytiger, Gregory , Author
Kang, Jaewoo, Author
Wells, Carrow I. , Author
Willson, Timothy M. , Author
Lienhard, Matthias1, 2, Author              Prasse, Paul2, AuthorBachmann, Ivo2, AuthorGanzlin, Julia2, AuthorBarel, Gal1, 2, Author              Herwig, Ralf1, 2, Author              Oprea, Tudor I., AuthorSchlessinger, Avner , AuthorDrewry, David H. , AuthorStolovitzky, Gustavo , AuthorWennerberg, Krister , AuthorGuinney, Justin , AuthorAittokallio, Tero, Author more..
Affiliations:
1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              
2Team ML-Med, The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, ou_persistent22              

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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

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Language(s): eng - English
 Dates: 2021-04-212021-06-03
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41467-021-23165-1
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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 12 Sequence Number: 3307 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723