Learning Graph Models for Retrosynthesis Prediction

Abstract

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of $53.7\%$, outperforming previous template-free and semi-template-based methods.

References in corpus (5)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
• A Graph to Graphs Framework for Retrosynthesis Prediction
• Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
• Retrosynthesis Prediction with Conditional Graph Logic Network

Cited by in corpus (5)

• Everything is Connected: Graph Neural Networks
• Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
• RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
• Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis
• DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis