Autoregressive latent diffusion for 3D molecule generation

Abstract:Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require molecular size to be specified (or predicted) separately before generation. This can be limiting for fragment-based molecule generation, central to drug discovery, where the size of the generated structure is itself part of the design problem. Autoregressive models determine size during generation and naturally support partial-structure conditioning, but balancing unconditional and fragment-conditioned generation remains challenging. We introduce KRONOS, a latent autoregressive diffusion framework that generates molecules in the latent space of a Unified AutoEncoder (UAE), jointly modeling molecular graph topology and geometry, while retaining the flexibility of autoregressive generation. We further introduce a mixed training strategy inspired by the Fill-in-the-Middle (FIM) paradigm, enabling a single left-to-right autoregressive model to support both unconditional and fragment-conditioned generation. Experiments on QM9 and GEOM-Drugs demonstrate strong unconditional generation performance and competitive fragment-conditioned generation.

Machine Learning (cs.LG)

arXiv:2607.09277 [cs.LG]

(or arXiv:2607.09277v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.09277 Focus to learn more arXiv-issued DOI via DataCite

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