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

Autor:	Debby D. Wang, Wenhui Wu, Ran Wang
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Binding affinity prediction Molecular representation Deep learning Interpretability Structure-based drug discovery Information technology T58.5-58.64 Chemistry QD1-999
Zdroj:	Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-15 (2024)
Druh dokumentu:	article
ISSN:	1758-2946
DOI:	10.1186/s13321-023-00795-9
Popis:	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. Graphical Abstract
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/9fc62943238b4e8ab19ee2bf67af39de Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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