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

Debby D. Wang

doi:10.1186/s13321-023-00795-9

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

2024-01-03

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DOI：10.1186/s13321-023-00795-9

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Abstract

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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.

Keywords:

Binding affinity prediction

Molecular representation

Deep learning

Interpretability

Structure-based drug discovery

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

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