Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab476
Keywords
protein-ligand interaction; drug discovery; binding site; binding affinity; binding pose; machine learning; deep learning
Funding
- Department of Energy [DE-AR0001213, DE -5C0020400, DE -5C0021303]
- National Science Foundation [DBI1759934, I1S1763246]
- National Institute of Health [R01GM093123]
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New drug production can take over 12 years and cost around $2.6 billion, with the COVID-19 pandemic emphasizing the need for more powerful computational methods in drug discovery. This review focuses on computational approaches using artificial intelligence (AI) to predict protein-ligand interactions, particularly in deep learning methods. The correlation between protein-ligand interaction aspects and the proposal to study them together could lead to more accurate machine learning-based prediction strategies.
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.
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