Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning

Area of Science:

• Computational chemistry
• Organic synthesis
• Artificial intelligence in chemistry

Background:

• Computer-aided synthesis planning often uses expert heuristics or machine learning (ML) models.
• Existing methods have limitations: expert heuristics are imperfect, and ML models struggle with rare reactions.
• Neural networks (NNs) typically require extensive data, limiting their predictive power for less common transformations.

Purpose of the Study:

• To investigate the synergistic potential of combining expert knowledge and ML for retrosynthesis.
• To improve the accuracy and scope of computer-aided reaction prediction.
• To enable reliable planning for both frequent and specialized chemical reactions.

Main Methods:

• Training neural networks (NNs) on literature data specifically mapped to expert-defined reaction rules.
• Developing a hybrid approach integrating heuristic chemical knowledge with ML predictions.
• Evaluating synthetic accuracy using a benchmark dataset encompassing diverse reaction types.

Main Results:

• The synergistic approach significantly outperformed standalone expert or ML methods in synthetic accuracy.
• NNs trained on rule-matched data demonstrated improved performance across a wider range of reactions.
• The combined strategy effectively handled rare and specialized chemical transformations, overcoming ML limitations.

Conclusions:

• Integrating expert chemical rules with ML models offers a powerful strategy for enhancing computer-aided synthesis planning.
• This synergistic approach improves prediction accuracy and expands applicability to specialized chemical reactions.
• Future work can leverage this hybrid methodology for more robust and versatile retrosynthetic analysis tools.