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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.29108 |
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| _version_ | 1866910268820291584 |
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| author | Guo, Yujia Kabeshov, Mikhail Le, Tat Hong Duong Genheden, Samuel Mijangos, Marco V. Voinarvoska, Varvara Bergonzini, Giulia Engkvist, Ola Kaski, Samuel |
| author_facet | Guo, Yujia Kabeshov, Mikhail Le, Tat Hong Duong Genheden, Samuel Mijangos, Marco V. Voinarvoska, Varvara Bergonzini, Giulia Engkvist, Ola Kaski, Samuel |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29108 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation Guo, Yujia Kabeshov, Mikhail Le, Tat Hong Duong Genheden, Samuel Mijangos, Marco V. Voinarvoska, Varvara Bergonzini, Giulia Engkvist, Ola Kaski, Samuel Machine Learning 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%. |
| title | Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.29108 |