Evidence map›Paper›PMID 42126060›Full record

ArticleJournal of chemical theory and computation2026

Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.

Sebastian Wingbermühle, Akash Deep Biswas, Domenico Bonanni, Tatiana Shugaeva, Davide Gadioli, Jakub Beránek, Giorgia Frumenzio, Lara Querciagrossa, Andrea Piserchia, Gianmarco Accordi and 7 more

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Sebastian WingbermühleDept. Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Solna 17165, Sweden.ORCID 0000-0001-5949-148X
Akash Deep BiswasEXSCALATE, Dompé Farmaceutici S.p.A., Naples 80131, Italy.
Domenico BonanniEXSCALATE, Dompé Farmaceutici S.p.A., Naples 80131, Italy.ORCID 0000-0001-9160-0914
Tatiana ShugaevaDept. Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Solna 17165, Sweden.ORCID 0009-0001-8147-0762
Davide GadioliDept. of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy.ORCID 0000-0002-0143-0737
Jakub BeránekIT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba 70800, Czech Republic.ORCID 0000-0003-3758-2836
Giorgia FrumenzioHPC Department, CINECA, Bologna 40033, Italy.
Lara QuerciagrossaHPC Department, CINECA, Bologna 40033, Italy.ORCID 0000-0002-6820-8102
Andrea PiserchiaHPC Department, CINECA, Bologna 40033, Italy.ORCID 0000-0002-6659-6922
Gianmarco AccordiDept. of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy.ORCID 0000-0001-8023-2108
Filippo LunghiniEXSCALATE, Dompé Farmaceutici S.p.A., Naples 80131, Italy.
Carmine TalaricoEXSCALATE, Dompé Farmaceutici S.p.A., Naples 80131, Italy.ORCID 0000-0003-4789-0955
Andrew EmersonHPC Department, CINECA, Bologna 40033, Italy.ORCID 0000-0001-9382-1868
Jan MartinovičIT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba 70800, Czech Republic.ORCID 0000-0001-7944-8956
Andrea Rosario BeccariEXSCALATE, Dompé Farmaceutici S.p.A., Naples 80131, Italy.ORCID 0000-0001-6830-2695
Gianluca PalermoDept. of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy.ORCID 0000-0001-7955-8012
Erik LindahlDept. Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Solna 17165, Sweden.ORCID 0000-0002-2734-2794

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale Virtual Screening (VS) campaigns of compound libraries can significantly speed up candidate selection in the early stages of drug discovery. The most promising drug candidates are identified by Scoring Functions (SFs), which enable VS campaigns to rank candidate compounds according to their estimated binding affinities. These SFs are typically trained on experimental data reflecting binding affinities (e.g., Dissociation Constant (

Indexed as

Molecular Dynamics SimulationProteinsBinding SitesDrug DiscoveryLigandsProtein BindingThermodynamicsWorkflowLigandsProteins

Identifiers

PMID42126060
PMCPMC13217541

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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.