Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

• Published in: Briefings in bioinformatics Vol. 23; no. 1
• Main Authors: Dhakal, Ashwin, McKay, Cole, Tanner, John J, Cheng, Jianlin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 17.01.2022; Oxford Publishing Limited (England)
• Subjects: Algorithms; Antiviral Agents - chemistry; Antiviral Agents - pharmacokinetics; Artificial intelligence; BASIC BIOLOGICAL SCIENCES; binding affinity; binding pose; binding site; Binding sites; Computer applications; COVID-19; COVID-19 - drug therapy; COVID-19 - metabolism; Deep Learning; Drug Discovery; Humans; Learning algorithms; Ligands; Machine learning; Pandemics; Protein Interaction Maps; Proteins; protein–ligand interaction; Review; SARS-CoV-2 - metabolism; Target recognition; binding affinity; binding pose; deep learning; drug discovery; protein–ligand interaction; machine learning; binding site
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab476

Abstract New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. 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–ligand 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–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand 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–ligand 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–ligand interactions.