Generative model predicts molecule breakups for drug design
RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis
Machine Learning
Summary
Designing new drugs often involves figuring out how to make complicated molecules step by step. This process, called retrosynthesis, starts with the final molecule and works backward to find simpler building blocks. The authors created RetroGEF, a computer model that can predict how to break down a molecule into smaller parts by adding and changing atoms and bonds all at once. Unlike some older methods, RetroGEF doesn’t need a fixed size or order for these changes. Tests show RetroGEF can do this task better than previous methods.
What this means in practice
- •For pharmaceutical chemists: Generate possible reactants for target drug molecules to speed up synthesis route design.
- •For chemical engineers: Improve automated planning of chemical manufacturing by predicting molecular transformations dynamically.
Authors
Xiaozhuang Song, Xuemin Chen, Xinjian Zhao, Yaoyao Xu, Tianshu Yu
Abstract
Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves molecular graph transformations that can change both connectivity and graph size. These transformations may introduce reactant components absent from the target while revising the product-derived structure. To model these transformations, we propose RetroGEF, a flow-based generative model for single-step retrosynthesis. Starting from the target molecule, it constructs possible reactants by adding atoms and changing bonds in the molecular graph. RetroGEF models molecular transformations and changes in graph size within the same generative process, rather than relying on a fixed-size graph canvas. It learns this process directly from product--reactant pairs without requiring a prescribed edit order. Experiments on representative retrosynthesis benchmarks demonstrate that RetroGEF achieves state-of-the-art performance.