Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
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Computer Science > Machine Learning
arXiv:2605.29108 (cs)
[Submitted on 27 May 2026]
Title:Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
Authors:Yujia Guo, Mikhail Kabeshov, Tat Hong Duong Le, Samuel Genheden, Marco V. Mijangos, Varvara Voinarvoska, Giulia Bergonzini, Ola Engkvist, Samuel Kaski
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Abstract: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. Data-driven assessment systems often oversimplify the multi-objective nature of synthesis design and rely on proxy datasets, such as patent routes, rather than universally 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, and then fine-tuned with expert evaluations to produce both quantitative scores and interpretable qualitative categories: Good, Plausible, and Bad. The resulting system achieves a Spearman correlation coefficient of 0.78 and a Pearson correlation of 0.77 for category assessment prediction, and 60.2% top-1 ranking accuracy for score prediction, substantially outperforming the previous baseline of 17.5%.
| Comments: | 13 pages, 11 figures, ELLIS Unconference Workshop: Generative Models, LLMs, and the Future of Molecular AI (ML4Molecules 2025) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.29108 [cs.LG] |
| (or arXiv:2605.29108v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29108
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation, by Yujia Guo and 8 other authors
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