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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2507.21762 |
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| _version_ | 1866911083481006080 |
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| author | Xuan-Vu, Nguyen Armstrong, Daniel P Jončev, Zlatko Schwaller, Philippe |
| author_facet | Xuan-Vu, Nguyen Armstrong, Daniel P Jončev, Zlatko Schwaller, Philippe |
| contents | Retrosynthesis planning remains a central challenge in molecular discovery due to the vast and complex chemical reaction space. While traditional template-based methods offer tractability, they suffer from poor scalability and limited generalization, and template-free generative approaches risk generating invalid reactions. In this work, we propose TempRe, a generative framework that reformulates template-based approaches as sequence generation, enabling scalable, flexible, and chemically plausible retrosynthesis. We evaluated TempRe across single-step and multi-step retrosynthesis tasks, demonstrating its superiority over both template classification and SMILES-based generation methods. On the PaRoutes multi-step benchmark, TempRe achieves strong top-k route accuracy. Furthermore, we extend TempRe to direct multi-step synthesis route generation, providing a lightweight and efficient alternative to conventional single-step and search-based approaches. These results highlight the potential of template generative modeling as a powerful paradigm in computer-aided synthesis planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TempRe: Template generation for single and direct multi-step retrosynthesis Xuan-Vu, Nguyen Armstrong, Daniel P Jončev, Zlatko Schwaller, Philippe Machine Learning Retrosynthesis planning remains a central challenge in molecular discovery due to the vast and complex chemical reaction space. While traditional template-based methods offer tractability, they suffer from poor scalability and limited generalization, and template-free generative approaches risk generating invalid reactions. In this work, we propose TempRe, a generative framework that reformulates template-based approaches as sequence generation, enabling scalable, flexible, and chemically plausible retrosynthesis. We evaluated TempRe across single-step and multi-step retrosynthesis tasks, demonstrating its superiority over both template classification and SMILES-based generation methods. On the PaRoutes multi-step benchmark, TempRe achieves strong top-k route accuracy. Furthermore, we extend TempRe to direct multi-step synthesis route generation, providing a lightweight and efficient alternative to conventional single-step and search-based approaches. These results highlight the potential of template generative modeling as a powerful paradigm in computer-aided synthesis planning. |
| title | TempRe: Template generation for single and direct multi-step retrosynthesis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.21762 |