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  • 09월 20일 16시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제126회 대한화학회 학술발표회 및 총회 Substructure-based Neural Machine Translation for Retrosynthetic Prediction

등록일
2020년 9월 17일 14시 35분 00초
접수번호
1142
발표코드
PHYS.P-238 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
10월 21일(수) 17:30~18:00
발표형식
포스터
발표분야
Physical Chemistry
저자 및
공동저자
Umit Volkan Ucak, Juyong Lee*
Department of Chemistry, Kangwon National University, Korea
With the rapid improvement of machine translation approaches, neural machine translation has started to play an important role in retrosynthesis planning, which finds reasonable synthetic pathways for a target molecule. Previous studies showed that utilizing the sequence-to-sequence frameworks of neural machine translation is a promising approach to tackle the retrosynthetic planning problem. In this work, we recast the retrosynthetic planning problem as a language translation problem using a template-free sequence-to-sequence model. The model is trained in an end-to-end and a fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, we introduced a new way of representing a chemical reaction based on molecular fragments. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods.The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. Specifically, our approach predicts highly similar reactant molecules with an accuracy of 57.7%. In addition, our method yields more robust predictions than existing methods.

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