Single-step retrosynthesis
22 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Single-step retrosynthesis
| Trend | Dataset | Best Model | Paper | Code | Compare |
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USPTO-50k
Most implemented papers
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
pandegroup/reaction_prediction_seq2seq
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We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
Computer-Assisted Retrosynthesis Based on Molecular Similarity
We demonstrate molecular similarity to be a surprisingly effective metric for proposing and ranking one-step retrosynthetic disconnections based on analogy to precedent reactions.
Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun-Dai/GLN
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NeurIPS 2019
Retrosynthesis is one of the fundamental problems in organic chemistry.
State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architecture.
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions.
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
ml-jku/mhn-react
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Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
Dual-view Molecule Pre-training
microsoft/DVMP
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After pre-training, we can use either the Transformer branch (this one is recommended according to empirical results), the GNN branch, or both for downstream tasks.
Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
kaist-amsg/LocalRetro
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JACS Au 2021
Our model shows a promising 89. 5 and 99. 2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions.
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions
wangxr0526/RetroPrime
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Chemical Engineering Journal 2021
RetroPrime achieves the Top-1 accuracy of 64. 8% and 51. 4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset.
