Artificial intelligence has transformed the practice of drug discovery in the past decade. Various artificial intelligence techniques have been used in a wide range of applications  . In this perspective, we present major applications of AI in drug discovery and discuss the relevant AI techniques, covering most recent progress in AI-driven drug discovery  . We expect that the perspective will serve as a guide for researchers who are interested in working at this intersected area of artificial intelligence and drug discovery. We also provide a GitHub repository summarizing the surveyed papers as a learning resource, which will be regularly updated.
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 perspective, 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 in AI-driven drug discovery , we present the relevant AI techniques including model architectures and learning paradigms in the surveyed papers . We expect that the perspective will serve as a guide for researchers who are interested in working at this intersected area of artificial intelligence and drug discovery. We also provide a GitHub URL with the collection of papers and codes, if applicable, as a learning resource, which will be regularly updated.
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has transformed
(AI) has been transforming
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artificial intelligence
AI
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, such as virtual screening and drug design
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present major applications of AI in
first give an overview on
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the relevant AI techniques, covering most recent
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
A
, we present the relevant AI techniques including model architectures and learning paradigms in the surveyed papers
R
repository summarizing the surveyed papers
URL with the collection of papers and codes, if applicable,