Using GNN property predictors as molecule generators
This work addresses the challenge of computational molecule discovery for materials science and chemistry, offering an incremental improvement by leveraging existing GNN predictors without additional training.
The authors tackled the problem of generating molecular structures with specific electronic properties by inverting graph neural network (GNN) property predictors, achieving target property rates comparable to or better than state-of-the-art generative models while producing more diverse molecules.
Graph neural networks (GNNs) have emerged as powerful tools to accurately predict materials and molecular properties in computational discovery pipelines. In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties. Starting from a random graph or an existing molecule, we perform a gradient ascent while holding the GNN weights fixed in order to optimize its input, the molecular graph, towards the target property. Valence rules are enforced strictly through a judicious graph construction. The method relies entirely on the property predictor; no additional training is required on molecular structures. We demonstrate the application of this method by generating molecules with specific DFT-verified energy gaps and octanol-water partition coefficients (logP). Our approach hits target properties with rates comparable to or better than state-of-the-art generative models while consistently generating more diverse molecules.