期刊
BIOINFORMATICS
卷 35, 期 24, 页码 5354-5356出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz519
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资金
- National Natural Science Foundation of China [81773634]
- National Science & Technology Major Project 'Key New Drug Creation and Manufacturing Programme', China [2018ZX09711002]
- 'Personalized Medicines-Molecular Signature-based Drug Discovery and Development', Strategic Priority Research Programme of the Chinese Academy of Sciences [XDA12050201]
Motivation: The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. Results: KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases.
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