Improvements in chemical reaction pathway exploration algorithms and dataset generation.

Zhaojia Dong, Hanwen Zhang, Bowen Li, Sixuan Mi, Jiabin Yin, Jingbo Wang, Jianyi Ma, Tong Zhu

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Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Zhaojia Dong *Institute of Atomic and Molecular Physics, Sichuan University, Chengdu, 610065, China.

Hanwen Zhang *AI for Science Institute, Beijing, 100080, China. hanwen.zhang1018@gmail.com.

Bowen LiShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.

Sixuan MiShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.

Jiabin YinShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.

Jingbo WangSchool of Chemical Engineering, Sichuan University, Chengdu, 610065, China.

Jianyi MaInstitute of Atomic and Molecular Physics, Sichuan University, Chengdu, 610065, China. jianyima@scu.edu.cn.

Tong ZhuShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China. tzhu@lps.ecnu.ecnu.cn.

Funding

AI for Science Program of the Shanghai Municipal Commission of Economy and Informatization Grant No. 2025-GZL-RGZN-BTBX-01004National Natural Science Foundation of China Grant Nos. 92570205, 92461313, and 22361132538the ECNU Multifunctional Platform for Innovation No. 001the Open Research Fund of Suzhou Laboratory No. SZLAB-1508-2024-TS017

6 · The paper itself

Abstract

Chemical reaction networks provide a comprehensive framework for understanding complex reaction systems, in which reaction path exploration is a critical component. In this study, molecular structures are represented as bond-electron matrices, and reaction candidates are systematically enumerated through matrix transformations. Starting from more than 1,000 reactant molecules, diverse reaction pathways were generated and validated using DFT calculations, resulting in OrgReact, a dataset comprising 9,649 reactions. The dataset includes reactant, product, and transition-state structures, together with associated energetic information, and is intended to support data-driven studies of organic reaction pathways and machine learning models for molecular energies and forces.

Identifiers

PMID42527407

DOI10.1038/s41597-026-07771-6

PMCPMC13421536

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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.