Evidence map›Paper›PMID 40782065›Full record

ArticleJournal of chemical theory and computation2025

Molecule Graph Networks with Many-Body Equivariant Interactions.

Zetian Mao, Chuan-Shen Hu, Jiawen Li, Chen Liang, Diptesh Das, Masato Sumita, Kelin Xia, Koji Tsuda

Abstract read
PubMed Full text at PMC Publisher
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

8 authors.

Zetian MaoGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 2778561, Japan.
Chuan-Shen HuSchool of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore.ORCID 0000-0002-4476-7866
Jiawen LiGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 2778561, Japan.
Chen LiangGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 2778561, Japan.
Diptesh DasGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 2778561, Japan.
Masato SumitaRIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 351-0198, Japan.
Kelin XiaSchool of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore.ORCID 0000-0003-4183-0943
Koji TsudaGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 2778561, Japan.ORCID 0000-0002-4288-1606

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop

Identifiers

PMID40782065
PMCPMC12392447

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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.