Retrosynthetic crosstalk between single-step reaction and multi-step planning.

Area of Science:

• Computational chemistry
• Organic synthesis
• Machine learning in chemistry

Background:

• Retrosynthesis is crucial for designing drugs and materials.
• Current machine learning models excel at single-step predictions but struggle with multi-step route generation.
• Developing efficient multi-step retrosynthetic strategies remains a significant challenge.

Purpose of the Study:

• To improve multi-step retrosynthetic route generation by integrating single-step prediction models with planning algorithms.
• To comprehensively assess various combinations of planning algorithms and single-step models across diverse datasets.
• To evaluate synthetic routes based on both solvability and practical laboratory feasibility.

Main Methods:

• Exploration of diverse datasets and combinations of planning algorithms with single-step retrosynthesis models.
• Systematic analysis of different model and algorithm pairings.
• Evaluation of generated routes using metrics for solvability (completeness) and feasibility (practicality).

Main Results:

• The combination of models and algorithms yielding the highest solvability did not consistently produce the most feasible routes.
• A nuanced evaluation approach is necessary, considering both route completeness and laboratory applicability.
• Performance varied significantly across different datasets and metric combinations.

Conclusions:

• Integrating single-step retrosynthesis models with planning algorithms offers a pathway to enhanced multi-step route generation.
• The study highlights the critical need to balance computational efficiency (solvability) with practical laboratory execution (feasibility).
• These findings advance the understanding of computational retrosynthesis and its real-world applicability in chemical synthesis.