Machine Learning in Computer-Aided Synthesis Planning.

Area of Science:

• Chemistry
• Computer Science
• Machine Learning

Background:

• Traditional computer-aided synthesis planning (CASP) relied on expert rules, leading to incomplete and biased results.
• Large-scale reaction databases now enable data-driven approaches to chemical synthesis.

Purpose of the Study:

• To review recent machine learning (ML) advancements in CASP.
• To address key challenges in retrosynthetic planning and reaction product prediction.

Main Methods:

• Utilizing large reaction corpora for data-driven retrosynthesis.
• Employing learned synthetic complexity metrics and nearest neighbor models for disconnection prediction.
• Developing neural network-based models for forward reaction prediction (product anticipation).

Main Results:

• ML models can generate high-quality retrosynthetic disconnections.
• Forward prediction models effectively validate proposed reactions and predict byproducts.
• Data-driven CASP significantly improves the efficiency and accuracy of synthesis planning.

Conclusions:

• Machine learning is rapidly transforming CASP, moving beyond traditional methods.
• Standardization of data and evaluation metrics is crucial for future progress.
• ML offers significant opportunities for advancing organic synthesis and design.