CLRe: A Synergistic Dual-Engine Framework for One-Step Retrosynthesis Prediction

Area of Science:

• Computational chemistry
• Artificial intelligence in chemistry
• Chemical synthesis prediction

Background:

• One-step retrosynthesis prediction models face challenges due to random training orders and mismatches between sequence generation and chemical topology.
• Existing methods struggle with complex multi-path chemical reasoning and accurately predicting challenging reaction classes.

Purpose of the Study:

• To introduce Contrastive curriculum Learning for Retrosynthesis (CLRe), a novel framework designed to overcome the limitations of current retrosynthesis prediction models.
• To enhance the accuracy and robustness of AI models in predicting chemical synthesis pathways, particularly for complex reactions.

Main Methods:

• CLRe integrates self-supervised curriculum learning with topological buffering to optimize training.
• A label-free contrastive metric quantifies molecular complexity for improved training pacing.
• Label smoothing is adapted as a topological buffer to maintain search entropy for complex chemical reasoning.

Main Results:

• CLRe demonstrates consistent performance improvements on the USPTO-50K and USPTO-MIT datasets.
• The framework significantly reduces accuracy disparities across historically challenging reaction classes.
• CLRe effectively captures fine-grained structural complexity beyond standard reaction rules.

Conclusions:

• CLRe offers a robust strategy for enhancing AI-driven retrosynthesis prediction by combining data-driven sequence generation with chemical intuition.
• The framework provides a significant advancement in bridging the gap between computational models and the complexities of chemical synthesis.