期刊
CURRENT OPINION IN STRUCTURAL BIOLOGY
卷 72, 期 -, 页码 135-144出版社
CURRENT BIOLOGY LTD
DOI: 10.1016/j.sbi.2021.10.001
关键词
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资金
- National Natural Science Foundation of China [21575128, 81773632]
- Natural Science Foundation of Zhejiang Province [LZ19H300001]
- Key R&D Program of Zhejiang Prov-ince [2020C03010]
- Fundamental Research Funds for the Central Universities [2020QNA7003]
This paper introduces the molecular representation and assessment metrics used in DL-based de novo drug design, summarizes the features of each architecture, and prospects the potential challenges and future directions of DL-based molecular generation.
De novo drug design is the process of generating novel lead compounds with desirable pharmacological and physiochemical properties. The application of deep learning (DL) in de novo drug design has become a hot topic, and many DLbased approaches have been developed for molecular generation tasks. Generally, these approaches were developed as per four frameworks: recurrent neural networks; encoderdecoder; reinforcement learning; and generative adversarial networks. In this review, we first introduced the molecular representation and assessment metrics used in DL-based de novo drug design. Then, we summarized the features of each architecture. Finally, the potential challenges and future directions of DL-based molecular generation were prospected.
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