Evidence map›Paper›PMID 42057538›Full record

ReviewChemistry (Weinheim an der Bergstrasse, Germany)2026

Transforming Molecular Synthesis With Large Language Models.

Li-Cheng Xu, De-Xin Zhu, Miao-Jiong Tang, Ting-Ting Liu, Yi-Ze Liu, Chun-Hui Ma, Zhe-Xu Li, Shuo-Qing Zhang, Xin Hong

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chemistry (Weinheim an der Bergstrasse, Germany), 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

9 authors.

Li-Cheng XuShanghai Academy of Artificial Intelligence for Science, Shanghai, China.
De-Xin ZhuCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Miao-Jiong TangCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Ting-Ting LiuCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Yi-Ze LiuCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Chun-Hui MaCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Zhe-Xu LiCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Shuo-Qing ZhangCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-7617-3042
Xin HongCenter of Chemistry for Frontier Technologies, Stoddart Institute of Molecular Science, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0003-4717-2814

Funding

Department of Science and Technology of Zhejiang Province 2022R01005Fundamental Research Funds for the Central Universities 226-2025-00105National Key R&D Program of China 2022YFA1504301National Natural Science Foundation of China 22271253National Natural Science Foundation of China W25120004New Generation Artificial Intelligence-National Science and Technology Major Project 2025ZD0121905Open Research Fund of School of Chemistry and Chemical Engineering of Henan Normal University 2024Z01Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study SN-ZJU-SIAS-006State Key Laboratory of Physical Chemistry of Solid Surfaces 202210
6 · The paper itself

Abstract

The integration of large language models (LLMs) into molecular synthesis is rapidly transforming the field, offering a paradigm shift beyond traditional data-driven approaches. LLMs bring a unique combination of massive knowledge absorption, sophisticated contextual representation, and multi-step logical inference, holding the potential to intelligently automate the entire synthesis workflow. This review explores how LLMs are revolutionizing molecular synthesis by functioning across four key roles: (1) as interactive domain-specific knowledge bases for instant expert consultation; (2) as powerful tools for structuring unstructured reaction data from literature; (3) as versatile predictors for molecular properties and reaction outcomes; and (4) as the central "brain" in intelligent agent systems for autonomous synthesis planning and execution. By systematically outlining these advances, we aim to provide a comprehensive roadmap for chemists to leverage the distinctive capabilities of LLMs, thereby accelerating the journey toward a more automated and intelligent future for molecular synthesis.

Indexed as

chemical datasetlarge language modelsmachine learningmolecular synthesisreaction prediction

Identifiers

PMID42057538
PMCPMC13485235

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.