Deep generative molecular design reshapes drug discovery

• Published in: Cell reports. Medicine Vol. 3; no. 12; p. 100794
• Main Authors: Zeng, Xiangxiang, Wang, Fei, Luo, Yuan, Kang, Seung-gu, Tang, Jian, Lightstone, Felice C., Fang, Evandro F., Cornell, Wendy, Nussinov, Ruth, Cheng, Feixiong
• Format: Journal Article
• Language: English; Norwegian
• Published: United States Elsevier Inc 20.12.2022; Elsevier
• Subjects: Artificial Intelligence; Drug Discovery - methods; Review
• ISSN: 2666-3791; 2666-3791
• DOI: 10.1016/j.xcrm.2022.100794

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. Deep generative models hold great promise as powerful approaches for drug design. Zeng et al. review the current deep generative models and their applications in discovering small molecules and macromolecules, discuss the data and technical challenges, and highlight future directions in improving deep generative models for drug discovery communities.