Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generate molecules that satisfy target properties without explicit conditional generation. We use Graph Energy Based Models and a training approach that does not require property labels. We validated our approach on well-established chemical benchmarks, showing superior results to state-of-the-art methods and demonstrating robustness and efficiency towards de novo drug design.
View on arXiv@article{miglior2025_2502.12219,
title={ Towards Efficient Molecular Property Optimization with Graph Energy Based Models },
author={ Luca Miglior and Lorenzo Simone and Marco Podda and Davide Bacciu },
journal={arXiv preprint arXiv:2502.12219},
year={ 2025 }
}