Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
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Volume 6 Number 1 2024
REVIEWS (Open Access)
Advances in Molecular Docking for Allosteric Drug Discovery: Ensemble Modeling, Molecular Dynamics, and Artificial Intelligence
Shamsuddin Sultan Khan 1*, Tufael 2
Bioinfo Chem 6 (1) 1-15 https://doi.org/10.25163/bioinformatics.6110717
Submitted: 27 October 2024 Revised: 20 December 2024 Accepted: 29 December 2024 Published: 31 December 2024
Abstract
Allosteric drug discovery has increasingly shifted toward the center of structure-based drug design, challenging traditional assumptions underlying molecular docking. Classical docking approaches rely on static protein structures, yet growing evidence suggests that protein dynamics play a critical role in shaping allosteric binding sites and ligand recognition. This review synthesizes recent advances in molecular docking for allosteric drug discovery, with particular emphasis on ensemble docking, molecular dynamics integration, and artificial intelligence (AI)-driven methods. Across studies, ensemble docking approaches enable the identification of transient or cryptic allosteric sites that are often inaccessible in single static conformations. Integration with molecular dynamics simulations further captures conformational flexibility, improving the accuracy of docking predictions and ligand binding assessment. At the same time, AI-based tools, including structure prediction frameworks such as AlphaFold, have expanded structural coverage, although they frequently represent dominant conformations and may underrepresent dynamic allosteric states. Emerging hybrid strategies that combine AI, molecular dynamics, and multiscale approaches such as QM/MM offer a more comprehensive framework for modeling protein–ligand interactions. However, key challenges remain, including accurate prediction of allosteric effects, maintenance of physical realism, and translation of computational predictions into experimentally validated therapeutics. Overall, molecular docking is evolving from static structure-based methods toward dynamic, integrative approaches that better capture protein flexibility and allosteric regulation. These advances provide new opportunities for improving the discovery of allosteric modulators, although robust validation and methodological standardization remain essential for clinical translation.
Keywords: Allosteric modulation; Molecular docking; Ensemble modeling; Artificial intelligence; Protein dynamics
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