Deep learning approaches for de novo drug design: An overview. (February 2022)

• Record Type: Journal Article
• Authors: Wang, Mingyang; Wang, Zhe; Sun, Huiyong; Wang, Jike; Shen, Chao; Weng, Gaoqi; Chai, Xin; Li, Honglin; Cao, Dongsheng; Hou, Tingjun
• Is Part Of: Current opinion in structural biology. Volume 72(2022)
• Page Start: 135
• Page End: 144
• Publication Date: 2022-02
• Subjects: Molecular biology -- Periodicals; 570
• DOI: 10.1016/j.sbi.2021.10.001
• Languages: English
• ISSNs: 0959-440X

Abstract

Abstract: 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 DL-based approaches have been developed for molecular generation tasks. Generally, these approaches were developed as per four frameworks: recurrent neural networks; encoder-decoder; 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.