Crowdsourced mapping extends the 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, Mehmet Tan, Chih-Han Huang, Edward S. C. Shih, Tsai-Min Chen, Chih-Hsun Wu, Wei-Quan Fang, Jhih-Yu Chen, Ming-Jing Hwang, Xiaokang Wang, Marouen Ben Guebila, Behrouz Shamsaei, Sourav Singh, Thin Nguyen, Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen, Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür, Hansaim Lim, Lei Xie, Georgi K. Kanev, Albert J. Kooistra, Bart A. Westerman, Panagiotis Terzopoulos, Konstantinos Ntagiantas, Christos Fotis, Leonidas Alexopoulos, Dimitri Boeckaerts, Michiel Stock, Bernard De Baets, Yves Briers, Yunan Luo, Hailin Hu, Jian Peng, Tunca Dogan, Ahmet S. Rifaioglu, Heval Atas, Rengul Cetin Atalay, Volkan Atalay, Maria J. Martin, Junhyun Lee, Seongjun Yun, Bumsoo Kim, Buru Chang, Gábor Turu, Ádám Misák, Bence Szalai, László Hunyady, Matthias Lienhard, Paul Prasse, Ivo Bachmann, Julia Ganzlin, Gal Barel, Ralf Herwig, Davor Oršolić, Bono Lučić, Višnja Stepanić, Tomislav Šmuc, Tudor I. Oprea, Avner Schlessinger, David H. Drewry, Gustavo Stolovitzky, Krister Wennerberg, Justin Guinney, Tero Aittokallio

doi:10.1038/s41467-021-23165-1

<jats:title>Abstract</jats:title><jats:p>Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.</jats:p>

Crowdsourced mapping extends the target space of kinase inhibitors

Description

Journal

Keywords

Details 詳細情報について

Export

Report a problem