Expert system for predicting reaction conditions: the Michael reaction case.

Area of Science:

• Computational chemistry
• Organic synthesis
• Chemoinformatics

Background:

• Generic chemical transformations can be achieved under various conditions.
• Identifying optimal synthetic protocols for specific reagents is challenging and resource-intensive.
• Michael β-addition reactions exemplify this, requiring careful selection of solvents and catalysts.

Purpose of the Study:

• To develop chemoinformatics models for predicting optimal reaction conditions for Michael additions.
• To reduce wasted time and resources in synthetic chemistry by forecasting reaction feasibility.
• To establish relationships between reagent structures and required reaction conditions.

Main Methods:

• Development of nine 2-class classification models based on 198 Michael reactions.
• Models discriminate feasibility with specific conditions (Lewis acid catalysts, hydrophobic solvents) and overall feasibility.
• Utilized machine learning methods (Support Vector Machine, Naive Bayes, Random Forest) with various descriptors (ISIDA fragments, MOLMAP, Electronic Effect Descriptors, CDK descriptors).

Main Results:

• Models demonstrated good predictive performance with balanced accuracy ranging from 0.7 to 1.0 in 3-fold cross-validation.
• Eight models predict compatibility with specific reaction condition options.
• A ninth model predicts overall Michael addition feasibility.
• Models were successfully applied to predict conditions for novel Michael reactions.

Conclusions:

• Chemoinformatics effectively predicts synthetic reaction conditions, optimizing resource allocation in organic chemistry.
• Developed machine learning models offer practical tools for synthetic chemists to identify feasible reaction pathways.
• The models are accessible online for predicting conditions of Michael addition reactions.