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

Area of Science:

• Computational Chemistry
• Chemical Informatics

Background:

• Automated construction of chemical structures is crucial for computational chemistry.
• Understanding substituent effects on chemical reactions requires efficient data generation and analysis.

Purpose of the Study:

• To develop and validate the Layered Molecular Editor (LME) method for automated chemical structure generation.
• To investigate the impact of substituent groups on chemical activity in Diels-Alder and Claisen rearrangement reactions.
• To demonstrate the integration capabilities of LME with computational chemistry workflows.

Main Methods:

• The Layered Molecular Editor (LME) method was developed for automated molecular modification.
• Open application programming interface (API) facilitates integration with other computational tools.
• Datasets for Diels-Alder and Claisen rearrangement reactions were generated using LME.
• Geometric structure optimization and transition state searching were performed using GFN2-xTB and iEIP.
• Machine learning methods were employed to analyze structure-activity relationships.

Main Results:

• LME successfully generated diverse chemical structures by modifying molecular moieties.
• The method enabled the creation of reaction datasets with varying substituent groups.
• Analysis revealed structure-activity relationships for Diels-Alder and Claisen rearrangement reactions.
• The GFN2-xTB, iEIP, and machine learning approaches were effectively integrated.

Conclusions:

• The Layered Molecular Editor (LME) provides a scalable and reliable platform for automated chemical structure generation.
• LME facilitates the screening of chemical reactions and the analysis of substituent effects.
• The developed method shows significant potential for integration into broader computational chemistry research.