Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches

• URL: http://arxiv.org/abs/2410.00709v1
• Date: Mon, 30 Sep 2024 03:40:49 GMT
• Title: Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
• Authors: Xuefeng Liu, Songhao Jiang, Xiaotian Duan, Archit Vasan, Chong Liu, Chih-chan Tien, Heng Ma, Thomas Brettin, Fangfang Xia, Ian T. Foster, Rick L. Stevens,
• Abstract summary: We have observed a rising trend in the use of traditional machine learning and deep learning models for predicting binding affinity. While prediction results are constantly improving, we also identify several open questions and potential directions that remain unexplored in the field. This paper could serve as an excellent starting point for machine learning researchers who wish to engage in the study of binding affinity.
• Score: 6.910316688468948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. The binding affinity, which refers to the strength of this interaction, is central to many important problems in bioinformatics such as drug design. An extensive amount of work has been devoted to predicting binding affinity over the past decades due to its significance. In this paper, we review all significant recent works, focusing on the methods, features, and benchmark datasets. We have observed a rising trend in the use of traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. While prediction results are constantly improving, we also identify several open questions and potential directions that remain unexplored in the field. This paper could serve as an excellent starting point for machine learning researchers who wish to engage in the study of binding affinity, or for anyone with general interests in machine learning, drug discovery, and bioinformatics.

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