ArticleACS omega2025
Benchmarking HelixFold3-Predicted Holo Structures for Relative Free Energy Perturbation Calculations.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- Protein-ligand affinity prediction via Jensen-Shannon divergence of molecular dynamics simulation trajectories.Biophysics and physicobiology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Free energy perturbation (FEP) calculations are a powerful tool for predicting binding affinities in drug discovery, but their accuracy heavily depends on accurate protein-ligand complex structures. While AlphaFold2 revolutionized protein structure prediction, its inability to predict holo structures limits its application in structure-based drug design. AlphaFold3 and its reproduction HelixFold3 demonstrated the ability to predict protein complexes with various binding partners, including small molecules. In this study, we evaluated HelixFold3's ability to predict protein-ligand complexes using eight targets from Wang et al.'s FEP benchmark set. Our analysis revealed that HelixFold3 outperformed the existing methods, including AlphaFold2, in predicting binding site conformations. Notably, the prediction of holo structures yielded a higher binding site accuracy compared to apo structures. FEP calculations using both HelixFold3-predicted holo and apo structures achieved accuracy comparable to that of calculations using crystal structures. Furthermore, HelixFold3 successfully predicted complex structures for novel derivatives not present in its training data, and FEP calculations using these predicted structures maintained reliable accuracy. These results suggest that HelixFold3-predicted structures can effectively substitute for crystal structures in early stage drug discovery.
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Registered trials
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