ArticleDigital discovery2025
A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks.
Article in Digital discovery, 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
- FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.Nature computational science · 2026Article
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- LinkLlama: Enabling Large Language Model for Chemically Reasonable Linker Design.bioRxiv : the preprint server for biology · 2026Article
- More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning.Journal of chemical information and modeling · 2026Article
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 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
- Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity?Journal of chemical information and modeling · 2025Article
- Polaris Challenge: Data-Driven Priors to Improve Docking for Pose Prediction.Journal of chemical information and modeling · 2025Article
- Article
- GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data.bioRxiv : the preprint server for biology · 2025Article
- Simpatico: accurate and ultra-fast virtual drug screening with atomic embeddings.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data for training and testing their parameters. In this work, we show that one of the widely-used datasets, PDBbind, suffers from several common structural artifacts of both proteins and ligands, which may compromise the accuracy, reliability, and generalizability of the resulting SFs. Therefore, we have developed a series of algorithms organized in a semi-automated workflow, HiQBind-WF, that curates non-covalent protein-ligand datasets to fix these problems. We also used this workflow to create an independent data set, HiQBind, by matching binding free energies from various sources including BioLiP, Binding MOAD and Binding DB with co-crystalized ligand-protein complexes from the PDB. The resulting HiQBind workflow and dataset are designed to ensure reproducibility and to minimize human intervention, while also being open-source to foster transparency in the improvements made to this important resource for the biology and drug discovery communities.
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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.