A Review: Drug Discovery Methods Based on Artificial Intelligence

Abstract

The discovery of drugs is recognized as a lengthy, highly costly, and extremely complex process. For example, some traditional drug discovery methods consist of millions of trials to get a druggable compound to the market. Drug discovery based on artificial intelligence can be a prompt, low-cost, and effective way to streamline drug discovery. Although some works have been proposed to use artificial intelligence tools for drug discovery, few people summarize these advances in a systematic way. In this paper, we propose an organized and comprehensive review that outlines a broad range of appliances of artificial intelligence in drug discovery including harnessing virtual screening and molecular docking techniques, utilizing pathway networks for repurposing existing drugs, lead identification, biomarker research, identification of the target, diverse variety of artificial intelligence and their comparison, etc. In addition, we shed light on predicted limitations and challenges in drug discovery based on artificial intelligence, as well as sketch the strategies to harness its potential for upcoming drug design endeavors.