ArticleThe journal of physical chemistry. B2026
Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.
Article in The journal of physical chemistry. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Mutation-Aware Machine Learning Framework for Predicting Binding Affinity of Nirmatrelvir Analogs Targeting Coronavirus Main Proteases.Molecules (Basel, Switzerland) · 2026Article
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- LINKER:Journal of chemical information and modeling · 2026Article
- From data to scent: validating an ensemble AI model that predicts novel insect odourant interactions.Journal of molecular modeling · 2026Article
- PLATE-VS: a web server for protein-ligand assay curation and cross-target virtual screening datasets.Nucleic acids research · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- The LiPP Benchmark Set for Modeling Lipid-Protein Complexes: Comparison of Co-Folding and Docking Methods.Journal of chemical information and modeling · 2026Article
- Pocket-Surface Discrete Differential Geometry as a Leakage-Robust Feature Class for Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- GRIPHIN: grids of pharmacophore interaction fields for affinity prediction.Journal of cheminformatics · 2026Article
- PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.BMC genomics · 2026Article
- BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.Journal of chemical information and modeling · 2026Article
- More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning.Journal of chemical information and modeling · 2026Article
- Addendum: Data splitting against information leakage with DataSAIL.Nature communications · 2026Article
- Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction.Scientific reports · 2026Article
- Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.The journal of physical chemistry. B · 2026Article
- Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.Scientific reports · 2025Article
- Polaris Challenge: Data-Driven Priors to Improve Docking for Pose Prediction.Journal of chemical information and modeling · 2025Article
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
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7 authors.
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Abstract
The majority of machine learning scoring functions used in drug discovery for predicting protein-ligand binding poses and affinities have been trained on the PDBBind data set. However, it is unclear whether these new scoring functions are actually an improvement over traditional models since often the training and test sets are cross-contaminated with proteins and ligands with high similarity, and hence, they may not perform comparably well in binding prediction of unrelated protein-ligand complexes. In this work, we have carefully prepared a new split of the PDBBind data set to control for data leakage, defined as proteins and ligands with high sequence and structural similarity. The resulting leak-proof (LP)-PDBBind data are used to retrain four popular SFs: AutoDock Vina, Random Forest (RF)-Score, InteractionGraphNet (IGN), and DeepDTA, to better test their capabilities when applied to new protein-ligand complexes. In particular, we have formulated a new independent data set, BDB2020+, by matching high-quality binding free energies from BindingDB with cocrystallized ligand-protein complexes from the PDB that have been deposited since 2020. Based on all of the benchmark results, the retrained models using LP-PDBBind consistently perform better, with IGN especially being recommended for scoring and ranking applications for new protein-ligand systems.
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