Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Artificial Intelligence

Background:

• Chemical reactions are fundamental to organic chemistry and drug design.
• AI models offer new possibilities for reaction innovation and synthesis pathway discovery.
• Effective machine learning requires robust data representation and feature engineering for chemical reactions.

Purpose of the Study:

• To comprehensively review established reaction featurization approaches for machine learning.
• To provide insights into selecting appropriate representations and designing features for diverse chemical tasks.
• To critically evaluate methods for bridging different data representations and explore new frontiers in reaction pretraining.

Main Methods:

• Review of established reaction featurization techniques.
• Analysis of molecular representations including SMILES, molecular fingerprints, and molecular graphs.
• Evaluation of physics-based properties for reaction feature engineering.
• Critical assessment of methods for inter-representation gap bridging.
• Introduction to novel chemical reaction pretraining concepts.

Main Results:

• Detailed elaboration on the advantages and limitations of various featurization methods (SMILES, fingerprints, graphs, physics-based properties).
• Identification of key considerations for selecting appropriate representations for specific machine learning tasks in chemistry.
• Evaluation of strategies to harmonize different chemical data representations.
• Proposal of chemical reaction pretraining as a promising new research direction.

Conclusions:

• Robust featurization is essential for successful machine learning in chemical reaction design and optimization.
• A critical understanding of different representation methods and their trade-offs is necessary.
• Novel pretraining approaches hold significant potential for advancing AI in chemistry.