Evidence map›Paper›PMID 40160764›Full record

ArticleACS omega2025

Benchmarking HelixFold3-Predicted Holo Structures for Relative Free Energy Perturbation Calculations.

Kairi Furui, Masahito Ohue

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025
    Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kairi FuruiDepartment of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan.ORCID https://orcid.org/0000-0003-1097-0003
Masahito OhueDepartment of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan.ORCID https://orcid.org/0000-0002-0120-1643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40160764
PMCPMC11947795

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Registered trials

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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.