Crowdsourced mapping of unexplored target space of kinase inhibitors

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s41467-021-23165-1.pdf

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2021-06-01

Author

Cichonska, Anna
Ravikumar, Balaguru
Allaway, Robert J.
Wan, Fangping
Park, Sungjoon
Isayev, Olexandr
Li, Shuya
Mason, Michael
Lamb, Andrew
Tanoli, Ziaurrehman
Jeon, Minji
Kim, Sunkyu
Popova, Mariya
Capuzzi, Stephen
Zeng, Jianyang
Dang, Kristen
Koytiger, Gregory
Kang, Jaewoo
Wells, Carrow I.
Willson, Timothy M.
Oprea, Tudor I.
Schlessinger, Avner
Drewry, David H.
Stolovitzky, Gustavo
Wennerberg, Krister
Guinney, Justin
Aittokallio, Tero
TAN, MEHMET
Huang, Chih-Han
Shih, Edward S. C.
Chen, Tsai-Min
Wu, Chih-Hsun
Fang, Wei-Quan
Chen, Jhih-Yu
Hwang, Ming-Jing
Wang, Xiaokang
Ben Guebila, Marouen
Shamsaei, Behrouz
Singh, Sourav
Nguyen, Thin
Karimi, Mostafa
Wu, Di
Wang, Zhangyang
Shen, Yang
Ozturk, Hakime
ÖZKIRIMLI ÖLMEZ, ELİF
ÖZGÜR TÜRKMEN, ARZUCAN
Lim, Hansaim
Xie, Lei
Kanev, Georgi K.
Kooistra, Albert J.
Westerman, Bart A.
Terzopoulos, Panagiotis
Ntagiantas, Konstantinos
Fotis, Christos
Alexopoulos, Leonidas
Boeckaerts, Dimitri
Stock, Michiel
De Baets, Bernard
Briers, Yves
Luo, Yunan
Hu, Hailin
Peng, Jian
DOĞAN, TUNCA
RİFAİOĞLU, AHMET SÜREYYA
Atas, Heval
ATALAY, RENGÜL
Atalay, Mehmet Volkan
Martin, Maria J.
Lee, Junhyun
Yun, Seongjun
Kim, Bumsoo
Chang, Buru
Turu, Gabor
Misak, Adam
Szalai, Bence
Hunyady, Laszlo
Lienhard, Matthias
Prasse, Paul
Bachmann, Ivo
Ganzlin, Julia
Barel, Gal
Herwig, Ralf
Orsolic, Davor
Lucic, Bono
Stepanic, Visnja
Smuc, Tomislav

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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

URI

https://hdl.handle.net/11511/93987

Journal

NATURE COMMUNICATIONS

DOI

https://doi.org/10.1038/s41467-021-23165-1

Collections

Department of Computer Engineering, Article

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Citation Formats

A. Cichonska et al., “Crowdsourced mapping of unexplored target space of kinase inhibitors,” NATURE COMMUNICATIONS, vol. 12, no. 1, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93987.