Title: Scalable Autoregressive 3D Molecule Generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: 3D molecular generative models, diffusion, autoregression

Abstract: Generative models of 3D molecular structure play a rapidly growing role in the design and simulation of molecules. Diffusion models currently dominate the space of 3D molecule generation, while autoregressive models have trailed behind. In this work, we present Quetzal, a simple but scalable autoregressive model that builds molecules atom-by-atom in 3D. Treating each molecule as an ordered sequence of atoms, Quetzal combines a causal transformer that predicts the next atom's discrete type with a smaller Diffusion MLP that models the continuous next-position distribution. Compared to existing autoregressive baselines, Quetzal achieves substantial improvements in generation quality and is competitive with the performance of state-of-the-art diffusion models. In addition, by reducing the number of expensive forward passes through a dense transformer, Quetzal enables significantly faster generation speed, as well as exact divergence-based likelihood computation. Finally, without any architectural changes, Quetzal natively handles variable-size tasks like hydrogen decoration and scaffold completion. We hope that our work motivates a perspective on scalability and generality for generative modelling of 3D molecules. Code is available at https://anonymous.4open.science/r/quetzal-5BD3.

TL;DR: We apply per-token diffusion to 3D molecular generative modelling, achieving a scalable autoregressive 3D generative model competitive with pure diffusion.