Enhancing Retrosynthesis with Conformer: A Template-Free Method
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DiffER: Categorical Diffusion for Chemical Retrosynthesis
Leveraging Large Language Models for enzymatic reaction prediction and characterization
Leveraging Large Language Models as Knowledge-Driven Agents for Reliable Retrosynthesis Planning
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.