Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases.

Weiliang Luo, Heather J Kulik

Abstract readPreprint

In one paragraph

Article in ArXiv, 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.

Weiliang LuoDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Heather J KulikDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Funding

Revealing Nature's Blueprints for Single-Site Catalysis of C-H Activation with First-principles Modeling and Machine LearningR35GM152027 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Heather J. Kulik · 2024 to 2026

$1.2M

NIGMS NIH HHS R35 GM152027

6 · The paper itself

Abstract

Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce their cost, but enzymes introduce new challenges not faced by NNPs for small molecules, including large system sizes, implicit-solvent environments, substantial polarization, and charge transfer. Here, we demonstrate an integrated software framework for efficient training of NNPs for mechanistic study of enzymes with demonstrations on QM cluster models of

Identifiers

PMID42428024

PMCPMC13345586

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LicenceCC BY-NC-ND

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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.