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Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics

The Journal of Chemical Physics(2025)10.1063/5.0244453Source: DataRank Database

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics is a research paper published in The Journal of Chemical Physics (2025). On theSindex it has a DataRank of 0. It has been cited 10 times.

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Open Access10 citations

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Enrichment:Funding (8 grants)OA: greenTopics (14)IDs (PubMed, PMC)Annotations (1)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Chatipat Lorpaiboon ORCID,Spencer C. Guo ORCID,Jonathan Weare ORCID,Aaron R. Dinner ORCID,Zihan Pengmei

Live enrichment

PubMed:39873278 PMC:PMC11779506

National Institute of General Medical Sciences

Grant: R35 GM136381

National Science Foundation

Grant: DMS-2054306

National Science Foundation Graduate Research Fellowship Program

Grant: 2140001

Graduate Research Fellowship Program (GRFP)

National Institutes of Health

Grant: HEI 1S10OD028655-0

NIH HHS

Grant: S10 OD028655

National Institutes of Health

Grant: 5R35GM136381-04

Rare-event simulation and analysis for elucidating mechanisms of development and disease

National Institutes of Health

Grant: 1S10OD028655-01

Beagle-3: A Shared GPU Cluster for Biomolecular Sciences

National Science Foundation

Grant: 2054306

DMS/NIGMS 1: Design and Analysis of Machine Learning Approaches for Long Timescale Prediction from Short Trajectory Data

greenLicense: cc-by

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11722521/pdf/nihpp-2409.19838v2.pdf

publisherhttps://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0244453/20364502/044107_1_5.0244453.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11779506/

https://doi.org/10.1063/5.0244453

https://dx.doi.org/10.48550/arxiv.2409.19838

Fields of Study

02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

Chemical Physics (physics.chem-ph)FOS: Computer and information sciencesComputer Science - Machine LearningFOS: Physical sciencesComputational Physics (physics.comp-ph)Quantitative Biology - Quantitative MethodsMachine Learning (cs.LG)ARTICLESPhysics - Chemical PhysicsFOS: Biological sciencesPhysics - Computational PhysicsQuantitative Methods (q-bio.QM)