Problem
Research questions and friction points this paper is trying to address.
Molecular Graph Generation
Latent Space
Flow Matching
Graph Generative Models
Computational Efficiency
Latent Flow Matching for Molecular Graph Generation
🤖 AI Summary
This study addresses the high computational cost and low efficiency of traditional discrete graph generation methods for large-scale molecular graphs by proposing a latent space flow matching framework for molecular graph generation. The method leverages a pretrained variational autoencoder (VAE) to obtain high-fidelity latent representations and, for the first time, introduces flow matching into the whole-graph latent space for generation. Furthermore, it incorporates a latent classifier to enable validity-aware guidance, ultimately decoding the representations into high-quality molecular graphs. Experimental results demonstrate that the proposed framework achieves superior Fréchet ChemNet Distance (FCD) scores and validity across multiple molecular benchmarks, significantly improving the trade-off between generation quality and computational efficiency.
📝 Abstract
Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with high reconstruction fidelity. The generated representations, obtained through flow matching, are then decoded only at the final step. Across molecular benchmarks of increasing size, our approach achieves strong validity and FCD while offering a favorable quality-efficiency trade-off compared with state-of-the-art explicit graph generative models. One of the main advantages of this formulation is that the graph representation only needs to be learned once, after which the same one can be reused across multiple generative objectives without retraining. We demonstrate generation guided by molecular properties and further introduce validity-aware generation though a classifier learned directly in latent space. All code will be made available upon acceptance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Latent Flow Matching
Molecular Graph Generation
Variational Autoencoder
Validity-aware Generation
Property-guided Generation
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
