An Expert-Augmented Deep Learning Approach for Interpretable Synthesis Route Evaluation

Selecting efficient multi-step synthetic routes is a central challenge in organic synthesis, particularly in medicinal and process chemistry, where route choice directly impacts feasibility, cost, and development efficiency. While computer-aided synthesis planning (CASP) tools can generate diverse pathways, the evaluation of these generated routes remains challenging. Existing heuristic or data-driven scoring systems often oversimplify the multi-objective nature of synthesis design and rely on proxy datasets (e.g., patent routes) rather than universal grounded criteria. To address this, we introduce an expert-augmented, data-driven scoring framework that integrates machine learning with chemists’ domain knowledge for both numerical and explainable route assessment. A DeepSets-based model is trained using tree edit distance between reference and machine-generated routes, then fine-tuned with expert evaluations to produce both quantitative scores and interpretable qualitative categories (Good, Plausible, Bad). Validated against expert assessments, the resulting system achieves a Spearman correlation coefficient of 0.78 ± 0.05 and a Pearson correlation of 0.77 ± 0.06 regarding its category assessment prediction. When ranking routes by predicted score, the model achieves an overall 60.2% top-1 accuracy (vs. a 17.5% baseline) relative to reference routes, while expert validation confirms that 75% of sampled top-1 selections are feasible, demonstrating the model correctly identifies diverse, expert-validated alternatives beyond the reference.