| 학회 |
한국고분자학회 |
| 학술대회 |
2022년 봄 (04/06 ~ 04/08, 대전컨벤션센터) |
| 권호 |
47권 1호 |
| 발표분야 |
데이터 및 기계학습을 이용한 고분자 과학 |
| 제목 |
RetroTRAE: Retrosynthetic Translation of \\ Atomic Environments with Transformer |
| 초록 |
We present a new single-step retrosynthesis prediction method, viz. RetroTRAE, using fragment-based tokenization and the Transformer architecture. RetroTRAE mimics chemical reasoning and predicts reactant candidates by learning the changes of atom environments (AEs) associated with the chemical reaction. AEs are the ideal stand-alone chemically meaningful building blocks providing a high-resolution molecular representation. Describing a molecule with a set of AEs establishes a clear relationship between translated product-reactant pairs due to the conservation of atoms in the reactions. Our model achieved a top-1 accuracy of 58.3% on the USPTO test dataset. Additionally, the attention matrices of RetroTRAE are shown to capture chemical changes around reaction sites successfully. Our methodology offers a novel way of devising a retrosynthetic planning model using fragmental and topological descriptors as natural inputs for chemical translation tasks and opens new possibilities for developing other sequence-based machine-learning methods in chemistry. |
| 저자 |
Umit V. Ucak1, Islambek Ashyrmamatov2, Junsu Ko1, Juyong Lee2
|
| 소속 |
1Division of Chemistry and Biochemistry, 2Kangwon National Univ. |
| 키워드 |
- |
| E-Mail |
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