ReMODE: a deep learning-based web server for target-specific drug design

Journal of Cheminformatics

Software

Yafeng Deng¹  Dan Li²  Tingjun Hou²  Yu Kang²  Peichen Pan²  Chang-Yu Hsieh²  Mingyang Wang³  Jike Wang³  Gaoqi Weng³  Honglin Li⁴ 

[1] CarbonSilicon AI Technology Co., Ltd, 310018, Hangzhou, Zhejiang, People’s Republic of China;Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, 310058, Hangzhou, Zhejiang, People’s Republic of China;Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, 310058, Hangzhou, Zhejiang, People’s Republic of China;CarbonSilicon AI Technology Co., Ltd, 310018, Hangzhou, Zhejiang, People’s Republic of China;Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 200237, Shanghai, People’s Republic of China;

关键词: Deep learning;    De novo drug design;    Molecular generation;    Adversarial autoencoders;    Transfer learning;    Artificial intelligence;   

DOI  :  10.1186/s13321-022-00665-w

 received in 2022-09-14, accepted in 2022-12-01,  发布年份 2022

来源: Springer

【 摘 要 】

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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Fig. 1 (abstract P46).