TY - JOUR
T1 - Deep generative molecular design reshapes drug discovery
AU - Zeng, Xiangxiang
AU - Wang, Fei
AU - Luo, Yuan
AU - Kang, Seung gu
AU - Tang, Jian
AU - Lightstone, Felice C.
AU - Fang, Evandro F.
AU - Cornell, Wendy
AU - Nussinov, Ruth
AU - Cheng, Feixiong
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/12/20
Y1 - 2022/12/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85143407893&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2022.100794
DO - 10.1016/j.xcrm.2022.100794
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C2 - 36306797
AN - SCOPUS:85143407893
SN - 2666-3791
VL - 3
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 12
M1 - 100794
ER -