Towards Efficient Molecular Property Optimization with Graph Energy Based Models
This work addresses the problem of efficient molecular property optimization for de novo drug design, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the challenge of optimizing chemical properties in vast chemical space by developing a generative energy-based architecture for implicit property optimization, which generated molecules satisfying target properties without explicit conditional generation and showed superior results to state-of-the-art methods on established benchmarks.
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.