Published Paper Details:

The importance of Computer Aided Drug Discovery (CADD) has been consistently increasing over the past 20 years. Artificial Intelligence (AI) is one of the CADD methods, machine learning (ML) is one of its subtopics. In the past five years , many Artificial Intelligence in Drug Discovery (AIDD) approaches were employed towards drug discovery . This article includes discussion on the scope and limitations of AIDD . Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery 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 present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a G it Hub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.