  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.
