Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation

Journal of Medicinal Chemistry
Xutong LiMingyue Zheng

Abstract

The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.

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Citations

May 27, 2020·Expert Opinion on Drug Discovery·Rajan ChaudhariShuxing Zhang
Oct 31, 2019·Nucleic Acids Research·Patroklos SamarasMathias Wilhelm
Dec 4, 2019·Journal of Computer-aided Molecular Design·Filip Miljković, Jürgen Bajorath
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May 1, 2021·International Journal of Molecular Sciences·Bo QiangZhenming Liu
May 8, 2021·Expert Opinion on Drug Discovery·Mateus Sá Magalhães SerafimKathia Maria Honorio
Jul 19, 2021·Current Opinion in Chemical Biology·Fabio UrbinaSean Ekins
May 12, 2020·Journal of Chemical Information and Modeling·Yanxing WangZhen-Ming Liu
Jul 17, 2020·Journal of Medicinal Chemistry·Chenjing CaiJianfeng Pei

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