Cao Xiao, Tengfei Ma, et al.
PLoS ONE
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.
Cao Xiao, Tengfei Ma, et al.
PLoS ONE
Kai Yang, Xiang Li, et al.
AAAI 2017
Fei Wang, Noah Lee, et al.
AAAI/IAAI 2011
Yuheng Hu, Ajita John, et al.
ICWSM 2012