Challenging Complexity with Simplicity: Rethinking the Role of Single-Step Models in Computer-Aided Synthesis...

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Computer-assisted synthesis planning is crucial for accelerating drug discovery.
• Deep-learning models demonstrate high accuracy in single-step retrosynthetic predictions.
• The effectiveness of these models in multi-step retrosynthetic route planning requires evaluation.

Purpose of the Study:

• To compare advanced deep-learning models with a template enumeration approach for retrosynthetic route planning.
• To assess performance on a real-world drug molecule dataset.
• To determine the most efficient strategy for complex synthesis planning.

Main Methods:

• Comparison of intricate single-step deep-learning models against a straightforward template enumeration method.
• Utilized a heuristic-based retrosynthesis knowledge score with the template enumeration approach.
• Evaluated performance on a dataset of real-world drug molecules.

Main Results:

• Template enumeration surpassed advanced models in efficiency for searching the reaction space.
• The template enumeration method achieved a higher or comparable solve rate within the same timeframe.
• Despite superior single-step accuracy of deep-learning models, template enumeration proved more effective for route planning.

Conclusions:

• Efficiency and retrosynthesis knowledge are critical for successful retrosynthetic route planning.
• Simple template enumeration should be considered a valuable benchmark in future research.
• This effective strategy should be integrated with complex models for practical computer-assisted synthesis planning in drug discovery.