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Single-step retrosynthesis

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보존된 평가 기록: 35

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Hanjun-Dai/GLN

공식 코드

Retrosynthesis Prediction with Conditional Graph Logic Network

https://github.com/MIRALab-USTC/DD-RetroDCVAE

공식 코드

Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables

https://github.com/bigchem/synthesis

공식 코드

State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis

https://github.com/coleygroup/graph2smiles

공식 코드

Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction

https://github.com/dptech-corp/nag2g

공식 코드

Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis

https://github.com/gmum/retrogfn

공식 코드

RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets

https://github.com/igashov/retrobridge

공식 코드

RetroBridge: Modeling Retrosynthesis with Markov Bridges

https://github.com/microsoft/DVMP

공식 코드

Dual-view Molecule Pre-training

https://github.com/microsoft/syntheseus

공식 코드

RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets

https://github.com/ml-jku/mhn-react

공식 코드

Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction

https://github.com/molecule-one/megan

공식 코드

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

https://github.com/otori-bird/retrosynthesis

공식 코드

Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction

https://github.com/songtaoliu0823/fusionretro

공식 코드

FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

https://github.com/uta-smile/retrocomposer

공식 코드

RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction

https://github.com/zengkaipeng/UAlign

공식 코드

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment

https://github.com/Anonnoname/G2GT_2

G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training

https://github.com/MolecularAI/Chemformer

https://github.com/O-GNN/O-GNN

https://github.com/coleygroup/openretro

Learning Graph Models for Retrosynthesis Prediction

https://github.com/connorcoley/retrosim

https://github.com/kaist-amsg/LocalRetro

https://github.com/pandegroup/reaction_prediction_seq2seq

Retrosynthetic reaction prediction using neural sequence-to-sequence models

https://github.com/vsomnath/graphretro

Learning Graph Models for Retrosynthesis Prediction

https://github.com/wangxr0526/RetroPrime

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.