Evidence map›Paper›PMID 42856457›Full record

ArticlebioRxiv : the preprint server for biology2026

SMartini: automated small molecule parametrization for Martini 3 force field.

Danis Yangaliev, S Banu Ozkan

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

2 authors.

Danis YangalievDepartment of Physics, Arizona State University, Tempe, AZ 85287-1504, USA.
S Banu OzkanDepartment of Physics, Arizona State University, Tempe, AZ 85287-1504, USA.

Funding

Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutionsR01GM147635 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI OZKAN, SEFIKA BANU, SWINT-KRUSE, LISKIN · 2022 to 2025
$1.8M
NIGMS NIH HHS R01 GM147635
6 · The paper itself

Abstract

We present SMartini, an automated pipeline for deriving Martini 3 coarse-grained force-field parameters for arbitrary small molecules. Starting from a molecular structure or a SMILES string, the pipeline maps the all-atom molecule onto a coarse-grained bead representation and automatically fits both bonded and non-bonded parameters. Bonded parameters are obtained via Boltzmann inversion of atomistic molecular dynamics trajectories; successive rounds of coarse-grained simulation and distribution-matching updates then refine these estimates until the coarse-grained conformational ensemble reproduces the all-atom reference within tolerance. We validate the pipeline on a diverse set of small molecules including drug-like compounds, metabolites, and cofactors. Implemented as a modular sequence of scripts that can be orchestrated on high-performance computing clusters, SMartini makes automated parametrization of large ligand libraries practical and reduces the manual effort required for coarse-grained model development, enabling high-throughput coarse-grained simulations of protein-ligand systems within the Martini 3 ecosystem.

Identifiers

PMID42856457
PMCPMC13648609

What OpenQuestion holds

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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.