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Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments : RetroTRAE: retrosynthetic translation of atomic environments with Transformer

Cited 26 time in Web of Science Cited 34 time in Scopus

Ucak, Umit, V; Ashyrmamatov, Islambek; Ko, Jun Su; Lee, Ju Yong

Issue Date
Nature Publishing Group
Nature Communications, Vol.13 No.1, p. 1186
Reaction route planning remains a major challenge in organic synthesis. The authors present a retrosynthetic prediction model using the fragment-based representation of molecules and the Transformer architecture in neural machine translation. Designing efficient synthetic routes for a target molecule remains a major challenge in organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing a high-resolution molecular representation. Our approach mimics chemical reasoning, and predicts reactant candidates by learning the changes of atom environments associated with the chemical reaction. Through careful inspection of reactant candidates, we demonstrate atom environments as promising descriptors for studying reaction route prediction and discovery. Here, we present a new single-step retrosynthesis prediction method, viz. RetroTRAE, being free from all SMILES-based translation issues, yields a top-1 accuracy of 58.3% on the USPTO test dataset, and top-1 accuracy reaches to 61.6% with the inclusion of highly similar analogs, outperforming other state-of-the-art neural machine translation-based methods. Our methodology introduces a novel scheme for fragmental and topological descriptors to be used as natural inputs for retrosynthetic prediction tasks.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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