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A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks
Yingze Wang1, Kunyang Sun1, Jie Li1
1Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA 94720 USA.
Digital Discovery
|April 7, 2025
Summary
Scoring functions (SFs) for predicting protein-ligand binding energies can be inaccurate due to structural issues in common datasets. This study introduces HiQBind-WF and HiQBind to improve data quality and reliability for drug discovery.
Area of Science:
- Computational chemistry
- Structural biology
- Bioinformatics
Background:
- Scoring functions (SFs) are crucial for predicting protein-ligand binding energies.
- High-quality structural and binding assay data are essential for developing accurate SFs.
- Existing datasets like PDBbind contain structural artifacts affecting SF performance.
Purpose of the Study:
- To address structural artifacts in protein-ligand datasets.
- To develop a semi-automated workflow (HiQBind-WF) for curating these datasets.
- To create a high-quality, independent dataset (HiQBind) for SF development.
Main Methods:
- Developed a series of algorithms for a semi-automated curation workflow.
- Integrated data from BioLiP, Binding MOAD, and Binding DB with PDB structures.
- Implemented HiQBind-WF to fix structural artifacts in protein-ligand complexes.
Main Results:
- Identified common structural artifacts in widely-used protein-ligand datasets.
- Successfully curated non-covalent protein-ligand datasets using HiQBind-WF.
- Created the HiQBind dataset, enhancing data quality for SF training and testing.
Conclusions:
- The HiQBind-WF workflow improves the accuracy and reliability of protein-ligand datasets.
- The HiQBind dataset offers a more robust resource for developing and validating SFs.
- The open-source nature of HiQBind promotes transparency and reproducibility in drug discovery research.
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