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Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein\u2013ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein\u2013ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein\u2013ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein\u2013ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein\u2013ligand interactions.<\/jats:p>","DOI":"10.1093\/bib\/bbab476","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T09:29:28Z","timestamp":1634635768000},"source":"Crossref","is-referenced-by-count":265,"title":["Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4047-9947","authenticated-orcid":false,"given":"Ashwin","family":"Dhakal","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cole","family":"McKay","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John J","family":"Tanner","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA"},{"name":"Department of Chemistry, University of Missouri, Columbia, MO, 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-2853","authenticated-orcid":false,"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"issue":"9","key":"2022012000300596700_ref1","first-page":"1","article-title":"ATPsite: sequence-based prediction of ATP- binding 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