Evidence map›Paper›PMID 37042797›Full record

ArticleJournal of chemical information and modeling2023

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

Francesc Sabanés Zariquiey, Adrià Pérez, Maciej Majewski, Emilio Gallicchio, Gianni De Fabritiis

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Potential distribution theory of alchemical transfer.The Journal of chemical physics · 2025
    Article
  8. Article
  9. Alchemical Enhanced Sampling with Optimized Phase Space Overlap.Journal of chemical theory and computation · 2024
    Article
  10. Article
  11. Article
  12. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Francesc Sabanés ZariquieyComputational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.ORCID 0000-0002-5674-4127
Adrià PérezAcellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain.
Maciej MajewskiAcellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain.ORCID 0000-0003-2605-8166
Emilio GallicchioDepartment of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States.ORCID 0000-0002-2606-4913
Gianni De FabritiisComputational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.ORCID 0000-0003-3913-4877

Funding

OpenMM: Scalable biomolecular modeling, simulation, and machine learningR01GM140090 · NIGMS · STANFORD UNIVERSITY · PI Thomas Edward Markland · 2021 to 2026
$3.1M
NIGMS NIH HHS R01 GM140090
6 · The paper itself

Abstract

The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.

Indexed as

Molecular Dynamics SimulationEntropyLigandsProtein BindingReproducibility of ResultsThermodynamicsLigands

Identifiers

PMID37042797
PMCPMC10577236

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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.