Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Organic Synthesis

Background:

• Computer-aided synthesis planning tools exist but struggle with optimizing reaction conditions like solvents, catalysts, and reagents.
• Organic chemists traditionally rely on identifying functional groups, rings, and reaction categories for synthesis planning.

Purpose of the Study:

• To introduce Rxn-INSIGHT, an open-source algorithm designed to automate the selection of reaction conditions in organic synthesis.
• To streamline the process of identifying and classifying chemical reactions and their components.

Main Methods:

• Developed Rxn-INSIGHT, an algorithm utilizing the bond-electron matrix approach.
• Implemented core functions for reaction classification, naming, and extraction of functional groups, rings, and scaffolds.
• Assessed performance against a benchmark dataset for accuracy in classification and naming.

Main Results:

• Rxn-INSIGHT achieved over 90% accuracy in reaction classification and over 95% in reaction naming.
• Analysis revealed ring structures are crucial for selecting analogous reactions, with 35 unique rings covering 75% of a large dataset.
• The algorithm can suggest solvents, catalysts, and reagents for novel reactions in under a second.

Conclusions:

• Rxn-INSIGHT effectively automates key aspects of organic synthesis planning, mimicking expert chemist decision-making.
• The algorithm's ability to analyze ring structures enhances its capacity to find similar reactions and predict conditions.
• Rxn-INSIGHT offers a rapid and efficient solution for optimizing reaction conditions, even for novel synthetic pathways.