14 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Retrosynthesis Prediction with Conditional Graph Logic Network
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
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
Dual-view Molecule Pre-training
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
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
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.
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged.
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates.
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials.