Evaluating and clustering retrosynthesis pathways with learned strategy.

Area of Science:

• Computational Chemistry
• Machine Learning in Chemistry
• Organic Synthesis

Background:

• Computer-aided synthesis planning (CASP) tools generate numerous potential synthetic routes.
• Evaluating and prioritizing these pathways remains a significant challenge due to a lack of holistic assessment mechanisms.
• Existing methods often rely on simple heuristics, limiting strategic pathway selection.

Purpose of the Study:

• To develop a data-driven approach for evaluating the strategic quality of retrosynthesis pathways.
• To leverage machine learning, specifically a dynamic tree-structured long short-term memory (tree-LSTM) model, for pathway prioritization.
• To enhance the selection of optimal synthetic routes from CASP outputs.

Main Methods:

• Curated a large-scale retrosynthesis pathway database (238k patent-extracted, ~55M artificial pathways from ASKCOS).
• Trained a tree-LSTM model to differentiate between patent-extracted (strategic) and artificial pathways for the same target molecule.
• Utilized the model to learn strategic relationships within single-step reactions and encode pathway information into latent vectors.

Main Results:

• The tree-LSTM model achieved a 79.1% top-1 ranking accuracy in recognizing patent-extracted pathways.
• The model successfully learned to encode pathway-level strategic information.
• Generated latent vectors facilitate clustering of similar pathways, illustrating strategic diversity.

Conclusions:

• The developed tree-LSTM model provides an effective data-driven mechanism for evaluating and prioritizing retrosynthesis pathways.
• This approach enhances the utility of CASP programs by enabling systematic selection of strategically sound synthetic routes.
• The method aids in identifying diverse and high-quality pathways, advancing chemical synthesis planning.