ArticleCommunications chemistry2025
A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- Novel green synthesis of polyfunctionally substituted phthalazines promoted by visible light, DFT studies and molecular docking with antimicrobial and antibiofilm potency.Scientific reports · 2026Article
- Exploring quantum annealing for coarse-grained protein folding.Scientific reports · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
- Dahuang-mudanpi decoction mitigates ALI/ARDS pulmonary inflammation via multi-target regulation of HMGB1.Frontiers in pharmacology · 2026Article
- Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.Communications chemistry · 2025Article
- Nephroprotective Effects of Quercetin-Selenium Nanoparticles Against Glycerol-Induced AKI.International journal of molecular sciences · 2025Article
- Article
- GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data.bioRxiv : the preprint server for biology · 2025Article
- How residence time works in allosteric drugs.Current opinion in structural biology · 2025Review
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.