Layered Molecular Editor for Automatic Construction of Chemical Structure Data Sets and Its Application

Herein, the layered molecular editor (LME) method is developed to automatically construct chemical structures by modifying various moieties of molecules in chemical reactions, such as substituent groups. With an open application programming interface (API), LME could be widely applied in the screening of chemical reactions and the modification effect of substituent groups, integrating with other computational chemistry programs. By means of LME, data sets of Diels-Alder (DA) and Claisen rearrangement reactions with different substituent groups have been created from templates of substrates, and the relationships between their chemical activities and corresponding chemical structures are investigated by automatic geometric structure optimization and transition state searching using GFN2-xTB, iEIP, and machine learning methods. This shows the scaling ability and reliability of the developed LME method.

Authors

Institutions

Publication Details

Journal
The Journal of Physical Chemistry A
Published
2025-07-19
DOI
https://doi.org/10.1021/acs.jpca.5c02132
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Layered Molecular Editor for Automatic Construction of Chemical Structure Data Sets and Its Application

Ming Lei, Yangqiu Liu, Hexiang Qi, Zhewei Li et al.
The Journal of Physical Chemistry A
Machine Learning in Materials Science
article

Layered Molecular Editor for Automatic Construction of Chemical Structure Data Sets and Its Application

Ming Lei, Yangqiu Liu, Hexiang Qi, Zhewei Li, Xiaofan Shi
article en

Abstract

Herein, the layered molecular editor (LME) method is developed to automatically construct chemical structures by modifying various moieties of molecules in chemical reactions, such as substituent groups. With an open application programming interface (API), LME could be widely applied in the screening of chemical reactions and the modification effect of substituent groups, integrating with other computational chemistry programs. By means of LME, data sets of Diels-Alder (DA) and Claisen rearrangement reactions with different substituent groups have been created from templates of substrates, and the relationships between their chemical activities and corresponding chemical structures are investigated by automatic geometric structure optimization and transition state searching using GFN2-xTB, iEIP, and machine learning methods. This shows the scaling ability and reliability of the developed LME method.

The Journal of Physical Chemistry AVol. 129(30)
Beijing University of Chemical Technology (CN)
National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation
Openalex Percentile: Top 87%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Layered Molecular Editor for Automatic Construction of Chemical Structure Data Sets and Its Application — Ming Lei, Yangqiu Liu, et al. · The Journal of Physical Chemistry A (2025) | TGRS Research Map | TGRS