LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes

Area of Science:

• Chemoinformatics
• Computational Chemistry
• Data Science

Background:

• Growing chemical reaction data overwhelms traditional navigation methods.
• Demand for advanced tools to extract value from reaction data is increasing.
• Computer-Aided Synthesis Planning and reaction networks offer new data extraction approaches.

Purpose of the Study:

• To present LinChemIn, a Python toolkit for chemoinformatics operations on synthetic routes and reaction networks.
• To enable the combination, comparison, and analysis of synthetic routes from different sources.
• To provide an open and extensible framework for collaborative software development.

Main Methods:

• Development of a Python toolkit (LinChemIn) leveraging existing chemoinformatics and graph arithmetic packages.
• Implementation of new data models and functionalities for route-level analysis.
• Adoption of Object-Oriented Design principles for modularity, reusability, and testability.

Main Results:

• LinChemIn facilitates interconversion between data formats and models.
• The toolkit enables route comparison and the calculation of route descriptors.
• Its architecture supports code reusability, testing, refactoring, and external contributions.

Conclusions:

• LinChemIn allows users to combine and analyze synthetic routes from various tools.
• It serves as an open and extensible framework for community contributions and scientific discussion.
• Future development includes advanced route evaluation metrics and a comprehensive ecosystem of functionalities.