Retrosynthesis Prediction with Conditional Graph Logic Network
This work addresses the problem of computer-aided retrosynthesis for chemists and researchers, offering an incremental improvement over existing template-based models.
The paper tackles retrosynthesis prediction in organic chemistry by proposing a Conditional Graph Logic Network that learns to apply reaction templates based on chemical feasibility and strategy, achieving an 8.1% improvement over state-of-the-art methods on a benchmark dataset.
Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely on template-based models that define subgraph matching rules, but whether or not a chemical reaction can proceed is not defined by hard decision rules. In this work, we propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks that learns when rules from reaction templates should be applied, implicitly considering whether the resulting reaction would be both chemically feasible and strategic. We also propose an efficient hierarchical sampling to alleviate the computation cost. While achieving a significant improvement of $8.1\%$ over current state-of-the-art methods on the benchmark dataset, our model also offers interpretations for the prediction.