ArticleJournal of chemical information and modeling2025
Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fully Automatable Relative Binding Free Energy Calculations with Enhanced Sampling Using FAST/MBAR.Journal of chemical theory and computation · 2026Article
- Benchmarking Docking Protocols on Predicting Alternative Binding Modes.Journal of chemical information and modeling · 2026Article
- Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.Journal of chemical information and modeling · 2026Article
- Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.Journal of chemical theory and computation · 2026Article
- Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations.Communications chemistry · 2026Article
- BindFlow: A Free, User-Friendly Pipeline for Absolute Binding Free Energy Calculations Using Free Energy Perturbation or MM(PB/GB)SA.Journal of chemical theory and computation · 2026Article
- Evaluation of the ABCG2 Charge Model in Protein-Ligand Binding Free-Energy Calculations.Journal of chemical information and modeling · 2025Article
- Multi-Component Synthesis of New Fluorinated-Pyrrolo[3,4-International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modeling. The current study contributes to the quantification of the impact of initial structure modeling on the accuracy across a diverse set of activity cliff pairs. Along with providing a quantitative relation between the resolution of the crystal structure and free energy accuracy, we also demonstrate the incorporation of a secondary solvation tool (SOLVATE) to increase the free energy accuracy, especially when crystal waters are missing. The study also evaluates the reliability of AI-predicted structures in RBFE calculations, showing their effectiveness in predicting RBFE directionality and assigning nominal resolutions to the predicted structures based on free energy accuracy. These findings provide a set of recommendations for the development of more robust RBFE protocols, informing the use of structural data, solvation techniques, and AI-predicted protein models in drug discovery.
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