Deep generative molecular design reshapes drug discovery

Xiangxiang Zeng, Fei Wang, Yuan Luo, Seung gu Kang, Jian Tang, Felice C. Lightstone, Evandro F. Fang, Wendy Cornell, Ruth Nussinov, Feixiong Cheng*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Original languageEnglish
Article number100794
JournalCell Reports Medicine
Issue number12
StatePublished - 20 Dec 2022


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