Evidence map›Paper›PMID 40098324›Full record

ArticleJournal of chemical theory and computation2025

Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.

Kseniia Korchagina, Steven D Schwartz

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2025. 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.

Kseniia KorchaginaDepartment of Chemistry and Biochemistry, University of Arizona, 1306 E University Blvd, Tucson 85721, Arizona, United States.
Steven D SchwartzDepartment of Chemistry and Biochemistry, University of Arizona, 1306 E University Blvd, Tucson 85721, Arizona, United States.ORCID 0000-0002-0308-1059

Funding

Protein dynamics from femtoseconds to milliseconds as crafted by natural and laboratory evolution: towards enzyme designR35GM145213 · NIGMS · UNIVERSITY OF ARIZONA · PI STEVEN D SCHWARTZ · 2022 to 2026
$2.1M
NIGMS NIH HHS R35 GM145213
6 · The paper itself

Abstract

This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita-Bayliss-Hillman (MBH) reaction.

Identifiers

PMID40098324
PMCPMC11978486

What OpenQuestion holds

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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.