Evidence map›Paper›PMID 39835670›Full record

ArticleJournal of chemical information and modeling2025

Thermal Titration Molecular Dynamics: The Revenge of the Fragments.

Matteo Pavan, Silvia Menin, Andrea Dodaro, Gianluca Novello, Chiara Cavastracci Strascia, Mattia Sturlese, Veronica Salmaso, Stefano Moro

Abstract read
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. P2YACS pharmacology & translational science · 2025
    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

8 authors.

Matteo PavanMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.ORCID 0000-0001-6234-5934
Silvia MeninMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Andrea DodaroMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Gianluca NovelloMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Chiara Cavastracci StrasciaMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Mattia SturleseMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.ORCID 0000-0003-3944-0313
Veronica SalmasoMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Stefano MoroMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.ORCID 0000-0002-7514-3802

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations. However, the physicochemical characteristics of fragments limit the accuracy and reliability of computational predictions of their binding mode. This article presents a new Thermal Titration Molecular Dynamics (TTMD) protocol, a recently developed enhanced sampling method for qualitatively estimating protein-ligand-binding stability, specifically tuned for the refinement of fragment docking results. The protocol has been applied to eight pharmaceutically relevant targets on 12 different test cases, including ligands with very low molecular weight and structural complexity (MiniFrag/FragLites). In more than 80% of cases, TTMD successfully identified the native fragment binding mode among a set of docking poses, outperforming docking alone and proving to be a useful tool to assist the fragment screening and optimization process.

Indexed as

Molecular Dynamics SimulationProteinsTemperatureBinding SitesDrug DiscoveryLigandsMolecular Docking SimulationProtein BindingSmall Molecule LibrariesLigandsProteinsSmall Molecule Libraries

Identifiers

PMID39835670
PMCPMC11815869

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Registered trials

None linked

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.