Evidence map›Paper›PMID 41882323›Full record

ArticleMolecular diversity2026

Integrating modern computational techniques elevates binding mode identification for molecular fragments.

Sergei Evteev, Fedor Sizov, Anna Pastukhova, Alexey Ereshchenko, Denis Adjugim, Yan Ivanenkov

Abstract read
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In one paragraph

Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

6 authors.

Sergei EvteevDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation. saevteev@vniia.ru.
Fedor SizovDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.
Anna PastukhovaDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.
Alexey EreshchenkoDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.
Denis AdjugimDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.
Yan IvanenkovDukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular docking is a routine and essential procedure in computational drug discovery. However, it is well known that current techniques are still far from perfect, especially when being applied to molecular fragments. In this work, we combined advantages of traditional energy-based docking, a diffusion model, intermolecular interaction filters, and ML-assisted scoring function to achieve higher accuracy. We compiled a comprehensive dataset of molecular fragments to evaluate our new protocol - HybriDock - in comparison with three other docking algorithms including DiffDock, Matcha and AutoDock Vina. HybriDock demonstrated remarkable improvements over these methods, highlighting the advantage of combining modern molecular modeling approaches. Additionally, we analyzed the influence of fragment chemical properties and protein family on the ability to predict accurate binding modes and highlighted up-to-date limitations and future directions.

Indexed as

Computational BiologyDrug DiscoveryMolecular Docking SimulationProteinsAlgorithmsBinding SitesLigandsProtein BindingLigandsProteinsDiffusion modelIntermolecular interactionsMachine learningMolecular dockingMolecular fragments

Identifiers

What OpenQuestion holds

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Read underepoch 390

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.