  Artificial intelligence (AI) has been transforming the practice of drug
discovery in the past decade. Various AI techniques have been used in a wide
range of applications, such as virtual screening and drug design. In this
survey, we first give an overview on drug discovery and discuss related
applications, which can be reduced to two major tasks, i.e., molecular property
prediction and molecule generation. We then discuss common data resources,
molecule representations and benchmark platforms. Furthermore, to summarize the
progress of AI in drug discovery, we present the relevant AI techniques
including model architectures and learning paradigms in the papers surveyed. We
expect that this survey will serve as a guide for researchers who are
interested in working at the interface of artificial intelligence and drug
discovery. We also provide a GitHub repository
(https://github.com/dengjianyuan/Survey_AI_Drug_Discovery) with the collection
of papers and codes, if applicable, as a learning resource, which is regularly
updated.
