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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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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. 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IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data.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.
Keywords
DRUG, PHARMACOLOGY, PREDICTION, DISCOVERY, PACKAGE

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MLA
Cichonska, Anna, et al. “Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors.” NATURE COMMUNICATIONS, vol. 12, no. 1, 2021, doi:10.1038/s41467-021-23165-1.
APA
Cichonska, A., Ravikumar, B., Allaway, R. J., Wan, F., Park, S., Isayev, O., … IDG-DREAM Drug-Kinase Binding, on behalf of. (2021). Crowdsourced mapping of unexplored target space of kinase inhibitors. NATURE COMMUNICATIONS, 12(1). https://doi.org/10.1038/s41467-021-23165-1
Chicago author-date
Cichonska, Anna, Balaguru Ravikumar, Robert J. Allaway, Fangping Wan, Sungjoon Park, Olexandr Isayev, Shuya Li, et al. 2021. “Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors.” NATURE COMMUNICATIONS 12 (1). https://doi.org/10.1038/s41467-021-23165-1.
Chicago author-date (all authors)
Cichonska, Anna, 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, Tero Aittokallio, Dimitri Boeckaerts, Michiel Stock, Bernard De Baets, Yves Briers, and on behalf of IDG-DREAM Drug-Kinase Binding. 2021. “Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors.” NATURE COMMUNICATIONS 12 (1). doi:10.1038/s41467-021-23165-1.
Vancouver
1.
Cichonska A, Ravikumar B, Allaway RJ, Wan F, Park S, Isayev O, et al. Crowdsourced mapping of unexplored target space of kinase inhibitors. NATURE COMMUNICATIONS. 2021;12(1).
IEEE
[1]
A. Cichonska et al., “Crowdsourced mapping of unexplored target space of kinase inhibitors,” NATURE COMMUNICATIONS, vol. 12, no. 1, 2021.
@article{8717931,
  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.}},
  articleno    = {{3307}},
  author       = {{Cichonska, Anna and Ravikumar, Balaguru and Allaway, Robert J. and Wan, Fangping and Park, Sungjoon and Isayev, Olexandr and Li, Shuya and Mason, Michael and Lamb, Andrew and Tanoli, Ziaurrehman and Jeon, Minji and Kim, Sunkyu and Popova, Mariya and Capuzzi, Stephen and Zeng, Jianyang and Dang, Kristen and Koytiger, Gregory and Kang, Jaewoo and Wells, Carrow I. and Willson, Timothy M. and Oprea, Tudor I. and Schlessinger, Avner and Drewry, David H. and Stolovitzky, Gustavo and Wennerberg, Krister and Guinney, Justin and Aittokallio, Tero and Boeckaerts, Dimitri and Stock, Michiel and De Baets, Bernard and Briers, Yves and IDG-DREAM Drug-Kinase Binding, on behalf of}},
  issn         = {{2041-1723}},
  journal      = {{NATURE COMMUNICATIONS}},
  keywords     = {{DRUG,PHARMACOLOGY,PREDICTION,DISCOVERY,PACKAGE}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{18}},
  title        = {{Crowdsourced mapping of unexplored target space of kinase inhibitors}},
  url          = {{http://doi.org/10.1038/s41467-021-23165-1}},
  volume       = {{12}},
  year         = {{2021}},
}

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