Retro3D: A 3D-aware Template-free Method for Enhancing Retrosynthesis via Molecular Conformer Information
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912654889582592 |
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| author | Zhuang, Jiaxi Zhang, Yu Zhang, Yan Qian, Ying Zhou, Aimin |
| author_facet | Zhuang, Jiaxi Zhang, Yu Zhang, Yan Qian, Ying Zhou, Aimin |
| contents | Retrosynthesis plays a crucial role in the fields of organic synthesis and drug development, where the goal is to identify suitable reactants that can yield a target product molecule. Although existing methods have achieved notable success, they typically overlook the 3D conformational details and internal spatial organization of molecules. This oversight makes it challenging to predict reactants that conform to genuine chemical principles, particularly when dealing with complex molecular structures, such as polycyclic and heteroaromatic compounds. In response to this challenge, we introduce a novel transformer-based, template-free approach that incorporates 3D conformer data and spatial information. Our approach includes an Atom-align Fusion module that integrates 3D positional data at the input stage, ensuring correct alignment between atom tokens and their respective 3D coordinates. Additionally, we propose a Distance-weighted Attention mechanism that refines the self-attention process, constricting the model s focus to relevant atom pairs in 3D space. Extensive experiments on the USPTO-50K dataset demonstrate that our model outperforms previous template-free methods, setting a new benchmark for the field. A case study further highlights our method s ability to predict reasonable and accurate reactants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retro3D: A 3D-aware Template-free Method for Enhancing Retrosynthesis via Molecular Conformer Information Zhuang, Jiaxi Zhang, Yu Zhang, Yan Qian, Ying Zhou, Aimin Machine Learning Artificial Intelligence Retrosynthesis plays a crucial role in the fields of organic synthesis and drug development, where the goal is to identify suitable reactants that can yield a target product molecule. Although existing methods have achieved notable success, they typically overlook the 3D conformational details and internal spatial organization of molecules. This oversight makes it challenging to predict reactants that conform to genuine chemical principles, particularly when dealing with complex molecular structures, such as polycyclic and heteroaromatic compounds. In response to this challenge, we introduce a novel transformer-based, template-free approach that incorporates 3D conformer data and spatial information. Our approach includes an Atom-align Fusion module that integrates 3D positional data at the input stage, ensuring correct alignment between atom tokens and their respective 3D coordinates. Additionally, we propose a Distance-weighted Attention mechanism that refines the self-attention process, constricting the model s focus to relevant atom pairs in 3D space. Extensive experiments on the USPTO-50K dataset demonstrate that our model outperforms previous template-free methods, setting a new benchmark for the field. A case study further highlights our method s ability to predict reasonable and accurate reactants. |
| title | Retro3D: A 3D-aware Template-free Method for Enhancing Retrosynthesis via Molecular Conformer Information |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.12434 |