|Title||Deep generative molecular design reshapes drug discovery.|
|Publication Type||Journal Article|
|Year of Publication||2022|
|Authors||Zeng X, Wang F, Luo Y, Kang S-G, Tang J, Lightstone FC, Fang EF, Cornell W, Nussinov R, Cheng F|
|Journal||Cell Rep Med|
|Date Published||2022 Dec 20|
|Keywords||Artificial Intelligence, Drug Discovery|
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.
|Alternate Journal||Cell Rep Med|
|PubMed Central ID||PMC9797947|
Deep generative molecular design reshapes drug discovery.
Submitted by chz4003 on April 11, 2023 - 12:22pm
Institute of Artificial Intelligence for Digital Health