https://github.com/songtaoliu0823/fusionretro
공식 코드FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Retrosynthetic analysis is a pivotal synthetic methodology in organic chemistry that employs a reverse-engineering approach, initiating from the target compound and retroactively tracing potential synthesis routes and precursor molecules. This technique proves instrumental in sculpting efficient synthetic strategies for intricate molecules, thus catalyzing the evolution and progression of novel pharmaceuticals and materials.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.