Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
2021 · JACS Au
저자
Shuan Chen, 정유성 (Yousung Jung)
연구주제
Computational Drug Discovery Methods · Machine Learning in Materials Science
NTIS 과학기술표준분류
배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류(정책 연계 맵과 같은 방법).
AI 연구 키워드·방법
local reactivity 반응 템플릿 역합성 예측
attention mechanism 딥러닝
키워드 (OpenAlex)
retrosynthesis local reactivity global attention mechanism reaction templates chemical reaction prediction
초록
High Resolution Image Download MS PowerPoint Slide As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. Here we propose a local retrosynthesis framework called LocalRetro, motivated by the chemical intuition that the molecular changes occur mostly locally during the chemical reactions. This differs from nearly all existing retrosynthesis methods that suggest reactants based on the global structures of the molecules, often containing fine details not directly re…
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교수 1
연구주제 1
Computational Drug Discovery Methods