Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction.

Area of Science:

• Synthetic organic chemistry
• Computational chemistry
• Cheminformatics

Background:

• Understanding substrate and catalyst reactivity is crucial for advancing synthetic organic chemistry.
• Traditional mechanistic studies heavily rely on labor-intensive experimental data (kinetic, thermodynamic, spectroscopic).
• Linear Free Energy Relationships (LFERs) offer predictive power but have limitations with experimental constants and comprehensive data integration.

Purpose of the Study:

• To review data-driven strategies for investigating organic reaction mechanisms.
• To highlight the application of computational descriptors in mechanistic inference.
• To showcase how machine learning and cheminformatics address limitations in traditional methods.

Main Methods:

• Integration of cheminformatics and machine learning techniques.
• Utilizing computational descriptors for mechanistic inference.
• Exploring multiparameter strategies for complex reactivity analysis.

Main Results:

• Data-driven modeling provides powerful tools for predicting and interpreting organic reaction mechanisms.
• Machine learning effectively handles complex reactivities through multiparameter approaches.
• Computational descriptors are evolving for enhanced mechanistic insights.

Conclusions:

• Data-driven approaches, particularly machine learning, offer a more efficient and comprehensive alternative to traditional mechanistic studies.
• These methods advance the prediction and understanding of substrate and catalyst reactivities in organic synthesis.
• The review underscores the growing importance of computational tools in modern mechanistic investigations.