Cited by 19Computational Drug Discovery MethodsProtein Structure and DynamicsProtein Interaction Studies and Fluorescence Analysis

A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction

Open access
Wu, Ming-Hsiu, Xie, Ziqian, Zhi, Degui·2025·Communications Chemistry

Published version of a preprint

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s42004-025-01506-1

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Satorras, V. G., Hoogeboom, E. & Welling, M. E (n) equivariant graph neural networks. In International conference on machine learning, 9323–9332 (PMLR, 2021).
  2. Lin, Z. et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. BioRxiv 2022, 500902 (2022).
  3. Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R. & Jaakkola, T. Equibind: Geometric deep learning for drug binding structure prediction. In International conference on machine learning, 20503–20521 (PMLR, 2022).

Cited by

Sorted by citations

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
Published
2025-04-07
Volume
8
Issue
1
Pages
108-108
ISSN
2399-3669 (ISSN-L)
OpenAlex
W4409218613
Cited by
19
References
54
Citations by year
2026: 13 · 2025: 6
FWCI
9.82
Type
journal article
Language
en
Open access
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
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review