Crowdsourced mapping of unexplored target space of kinase inhibitors

Anna Cichońska, Balaguru Ravikumar, Robert J. Allaway, Fangping Wan, Sungjoon Park, Olexandr Isayev, Shuya Li, Michael Mason, Andrew Lamb, Ziaurrehman Tanoli, Minji Jeon, Sunkyu Kim, Mariya Popova, Stephen Capuzzi, Jianyang Zeng, Kristen Dang, Gregory Koytiger, Jaewoo Kang, Carrow I. Wells, Timothy M. Willson, Tudor I. Oprea, Avner Schlessinger, David H. Drewry, Gustavo Stolovitzky, Krister Wennerberg (), Justin Guinney () and Tero Aittokallio ()
Additional contact information
Anna Cichońska: HiLIFE, University of Helsinki
Balaguru Ravikumar: HiLIFE, University of Helsinki
Robert J. Allaway: Computational Oncology, Sage Bionetworks
Fangping Wan: Tsinghua University
Sungjoon Park: Korea University
Olexandr Isayev: Carnegie Mellon University
Shuya Li: Tsinghua University
Michael Mason: Computational Oncology, Sage Bionetworks
Andrew Lamb: Computational Oncology, Sage Bionetworks
Ziaurrehman Tanoli: HiLIFE, University of Helsinki
Minji Jeon: Korea University
Sunkyu Kim: Korea University
Mariya Popova: Carnegie Mellon University
Stephen Capuzzi: University of North Carolina
Jianyang Zeng: Tsinghua University
Kristen Dang: Computational Oncology, Sage Bionetworks
Gregory Koytiger: Immuneering Corporation
Jaewoo Kang: Korea University
Carrow I. Wells: University of North Carolina
Timothy M. Willson: University of North Carolina
Tudor I. Oprea: University of New Mexico School of Medicine
Avner Schlessinger: Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai
David H. Drewry: University of North Carolina
Gustavo Stolovitzky: IBM T J Watson Research Center, IBM
Krister Wennerberg: University of Copenhagen
Justin Guinney: Computational Oncology, Sage Bionetworks
Tero Aittokallio: HiLIFE, University of Helsinki

Nature Communications, 2021, vol. 12, issue 1, 1-18

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23165-1

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DOI: 10.1038/s41467-021-23165-1

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