Predicting reaction conditions: a data-driven perspective.

Area of Science:

• Synthetic Chemistry
• Computational Chemistry
• Machine Learning

Background:

• Optimizing reaction conditions is crucial for efficient, sustainable, and scalable chemical synthesis.
• Machine learning (ML) shows promise for predicting reaction conditions in computer-aided synthesis planning (CASP).
• Current ML models struggle with data quality, sparsity, reaction representation, and evaluation, often failing to outperform simple baselines.

Purpose of the Study:

• To critically review state-of-the-art ML techniques for reaction condition prediction.
• To identify innovations addressing key challenges in modeling chemical reactions.
• To demonstrate the impact of reaction representation on model performance.

Main Methods:

• Critical review of ML techniques for reaction condition prediction.
• Case study on heteroaromatic Suzuki-Miyaura reactions using US patent data (USPTO).
• Application of Condensed Graph of Reaction (CGR) representations as model inputs.

Main Results:

• Identified key innovations addressing challenges in ML for reaction condition prediction.
• Demonstrated that CGR-based inputs significantly enhance predictive power for Suzuki-Miyaura reactions.
• Showed improved model performance exceeding literature-derived popularity baselines.

Conclusions:

• Reaction representation is critical for improving ML models in CASP.
• CGR offers a powerful alternative for representing reactions, enhancing predictive accuracy.
• Future work should focus on data quality mitigation and advanced modeling strategies.