LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes

Area of Science:

• Computational chemistry
• Cheminformatics
• Data science in chemistry

Background:

• Computational tools are transforming chemical reactivity prediction.
• Integrating AI-generated insights with human expertise requires accessible interfaces.
• Analyzing large reaction data corpora presents challenges in data manipulation and interpretation.

Purpose of the Study:

• To introduce LinChemIn, an open-source Python toolkit for chemical reaction network analysis.
• To demonstrate how LinChemIn simplifies the manipulation and analysis of synthetic routes.
• To facilitate the interplay between artificial intelligence and human expertise in chemistry.

Main Methods:

• Development of an open-source Python toolkit (LinChemIn).
• Implementation of functionalities for merging, editing, mining, and analyzing reaction networks.
• Design of a flexible input interface to process diverse reaction route sources.
• Development of algorithms for extracting individual routes from synthetic trees.

Main Results:

• LinChemIn ensures chemical consistency across reaction network operations.
• The toolkit processes reaction routes from various sources, including predictive models and expert input.
• LinChemIn efficiently extracts and analyzes individual synthetic routes, identifying alternatives.
• Reduced operational barriers for accessing and analyzing complex synthetic route data.

Conclusions:

• LinChemIn effectively bridges the gap between AI-driven chemical predictions and domain specialist knowledge.
• The toolkit enhances the usability of computational chemistry tools for practical applications.
• LinChemIn promotes a more integrated approach to chemical research and development.