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Retrosynthesis

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.

Papers

Showing 2650 of 110 papers

TitleStatusHype
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Re-evaluating Retrosynthesis Algorithms with SyntheseusCode1
MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planningCode1
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain FeedbackCode1
Retro-fallback: retrosynthetic planning in an uncertain worldCode1
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
Retrosynthesis Prediction with Conditional Graph Logic NetworkCode1
A Bayesian algorithm for retrosynthesisCode1
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion GenerationCode1
Retrosynthetic Planning with Dual Value NetworksCode1
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic PlanningCode1
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template PredictionCode1
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug DiscoveryCode1
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield NetworksCode1
Learning Graph Models for Retrosynthesis PredictionCode1
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step RetrosynthesisCode1
MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif EditingCode1
G^2Retro as a Two-Step Graph Generative Models for Retrosynthesis PredictionCode1
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis ModelsCode0
DiffER: Categorical Diffusion for Chemical RetrosynthesisCode0
A Self-feedback Knowledge Elicitation Approach for Chemical Reaction PredictionsCode0
Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes through Reaction Template Sequence AnalysisCode0
RetroComposer: Composing Templates for Template-Based Retrosynthesis PredictionCode0
Leveraging Large Language Models for enzymatic reaction prediction and characterizationCode0
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural NetworksCode0
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