Artificial Intelligence in Drug Discovery: Applications and Techniques

• URL: http://arxiv.org/abs/2106.05386v2
• Date: Fri, 11 Jun 2021 00:57:41 GMT
• Title: Artificial Intelligence in Drug Discovery: Applications and Techniques
• Authors: Jianyuan Deng, Zhibo Yang, Dimitris Samaras, Fusheng Wang
• Abstract summary: Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. We first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks. We then discuss common data resources, molecule representations and benchmark platforms.
• Score: 33.59138543942538
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 repository\footnote{\url{https://github.com/dengjianyuan/Survey_AI_Drug_Discovery}} with the collection of papers and codes, if applicable, as a learning resource, which will be regularly updated.

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