Incorporating chemists’ insight with AI models for single-step retrosynthesis prediction
Retrosynthesis analysis is a critical task in organic chemistry and central to many important industries. It primarily involves decomposing a target molecule into commercially available molecules step by step. Since synthesis strategies can be quite diverse and strategic, retrosynthesis planning with expert knowledge has long been considered an “art.”
Recently, machine learning-based approaches have achieved promising results on this task, particularly in single-step retrosynthesis prediction. In retrosynthesis, a molecule can be represented as either a 2D graph or a 1D SMILES (simplified molecular-input line-entry system) sequence. SMILES is a notation