Faculty, Staff and Student Publications
Language
English
Publication Date
4-7-2025
Journal
Communications Chemistry
DOI
10.1038/s42004-025-01506-1
PMID
40195508
PMCID
PMC11977223
PubMedCentral® Posted Date
4-7-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction.
Keywords
Molecular modelling, Computational chemistry, Cheminformatics
Published Open-Access
yes
Recommended Citation
Ming-Hsiu Wu, Ziqian Xie, and Degui Zhi, "A Folding-Docking-Affinity Framework for Protein-Ligand Binding Affinity Prediction" (2025). Faculty, Staff and Student Publications. 672.
https://digitalcommons.library.tmc.edu/uthshis_docs/672