Machine Learning Enhanced Molecular Docking: Advances in Algorithms, Accuracy, and Drug Discovery Efficiency
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P127Keywords:
Molecular Docking, Machine Learning, Deep Learning, Drug Discovery, Virtual Screening, Bioinformatics, Scoring Functions, Computational ChemistryAbstract
Molecular docking is one of the core algorithms used in structure-based drug discovery, which makes it possible to compute binding orientations and affinities between small molecule ligand and a biological target. Standard docking algorithms are based on physics based scoring functions and heuristic search strategies, which can find the trade-off between predictive accuracy and computational efficiency difficult. In recent years, the field of machine learning (ML) has developed as a disruptive paradigm that is able to learn complicated, non-linear relationships using large-scale biochemical information. ML-based methods applied in conjunction with molecular docking have greatly improved the quality of algorithmic performance, improved prediction of binding affinity, and speeded up virtual screening pipelines. In this paper, a rigorous and in-depth review of the machine learning-based molecular docking methods is provided and centered on algorithmic developments, accuracy, and efficiency enhancements in pharmaceutical discovery processes. We address supervised, unsupervised, and deep learning methods that are used in pose prediction, optimization of any scoring function, and docking refinement. Moreover, this paper also points out hybrid models that combine bioinformatics algorithms and chemical models with the focus of integrative use of information technology and computational chemistry in earlier research on bioinformatics algorithms to molecular docking. It suggests a methodological framework which involves feature engineering, neural scoring functions and reinforcement learning based conformational search. They are experimental evaluations developed in the recent literature and analyzed to evaluate the improvements in the docking accuracy, enrichment factors, and computational speed. The last part is finally the discussion and challenges, limitations and research directions to be followed in the future which include model interpretability, data bias and generalization across dissimilar protein ligand systems. This article intends to provide a reference to a researcher and practitioners who want to capitalize on machine learning to enhance the efficiency of molecular docking and drug discovery.
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