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Structure-based, deep-learning models for protein-ligand binding affinity prediction.

ABSTRACT: The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.

SUBMITTER: Wang DD

PROVIDER: S-EPMC10765576 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Structure-based, deep-learning models for protein-ligand binding affinity prediction.

Wang Debby D DD Wu Wenhui W Wang Ran R

Journal of cheminformatics 20240103 1

The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been ...[more]

PMID: 38173000

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