Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911790443528192 |
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| author | Chen, Shuan Jung, Yousung |
| author_facet | Chen, Shuan Jung, Yousung |
| contents | Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meticulously assembling an extensive set of out-of-distribution (OOD) reactions. Our findings demonstrate that while template-free models exhibit potential in predicting precursors with novel synthesis rules, their top-10 exact-match accuracy in OOD reactions is strikingly modest (< 1%). Furthermore, despite the capability of generating novel reactions, our investigation highlights a recurring issue where more than half of the novel reactions predicted by template-free models are chemically implausible. Consequently, we advocate for the future development of template-free models that integrate considerations of chemical feasibility when navigating unexplored regions of reaction space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models Chen, Shuan Jung, Yousung Chemical Physics Machine Learning Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meticulously assembling an extensive set of out-of-distribution (OOD) reactions. Our findings demonstrate that while template-free models exhibit potential in predicting precursors with novel synthesis rules, their top-10 exact-match accuracy in OOD reactions is strikingly modest (< 1%). Furthermore, despite the capability of generating novel reactions, our investigation highlights a recurring issue where more than half of the novel reactions predicted by template-free models are chemically implausible. Consequently, we advocate for the future development of template-free models that integrate considerations of chemical feasibility when navigating unexplored regions of reaction space. |
| title | Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2403.03960 |