Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
Year: 2021
Type: review
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.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bbab476
Institution: University of Missouri
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,894
Awards: UNIVERSITY OF MISSOURI - NEW AWARD
CONTROL NUMBER: 2107-1570
PROJECT TITLE: DEEP LEARNING PREDICTION OF PROTEIN COMPLEX STRUCTURES
PROJECT DESCRIPTION: THIS COOPERATIVE AGREEMENT IS HEREBY AWARDED TO THE CURATORS OF THE UNIVERSITY OF MISSOURI WHO PROPOSES TO DEVELOP NOVEL DEEP LEARNING METHODS TO PREDICT INTER-PROTEIN AMINO ACID INTERACTIONS (CONTACTS) AND BUILD THREE-DIMENSIONAL STRUCTURES OF PROTEIN COMPLEXES., HIGH THROUGHPUT, ACCURATE GENE ANNOTATION THROUGH AI AND HPC-ENABLED STRUCTURAL ANALYSIS, III: Medium: Collaborative Research: Guiding Exploration of Protein Structure Spaces with Deep Learning, ABI Innovation: Deep learning methods for protein bioinformatics, G6301614861, Distance-based ab initio protein structure prediction, G8287020682, DEEP GREEN: STRUCTURAL AND FUNCTIONAL GENOMIC CHARACTERIZATION OF CONSERVED UNANNOTATED GREEN LINEAGE PROTEINS, G946059479
OpenAlex ID: W3215525389