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A Folding–Docking–Affinity framework for protein–ligand binding affinity prediction
Open accessPreprint — published as 10.1038/s42004-025-01506-1 · Paper page
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.
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Wu, M.-H., Xie, Z., & Zhi, D.
- Affiliations
- The University of Texas Health Science Center at Houston (US)
- Corresponding author
- Wu, Ming-Hsiu
- Zhi, Degui
- Venue
- Communications Chemistry
- Publisher
- openRxiv
- Published
- 2024-04-19
- Volume
- 8
- Issue
- 1
- Pages
- 108-108
- ISSN
- 2399-3669 (ISSN-L)
- PMC
- PMC11977223
- OpenAlex
- W4409218613
- Cited by
- 19
- References
- 2
- Citations by year
- 2026: 13 · 2025: 6
- FWCI
- 9.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- www.biorxiv.org/about/FAQ#license (vor)
- Locations
- Communications Chemistry (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-ligand binding affinity prediction, protein folding, protein-ligand docking, drug discovery
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