aiqbench analysis
This research proposes Latent Flow Matching as an efficient alternative to discrete graph generation, significantly reducing computational costs for large-scale molecular modeling by operating in a continuous latent space. The method decouples representation learning from generative objectives, allowing a single pretrained Variational Autoencoder to support multiple downstream tasks without retraining. By integrating validity-aware constraints directly into the latent classifier, the approach maintains high chemical accuracy while improving the quality-efficiency trade-off for drug discovery applications.
Latent Flow Matching for Molecular Graph Generation
arXiv:2610.06468v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.06468 [cs.LG]
(or arXiv:2610.06468v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.06468 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 247 words
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