用自回归方法高效生成3D分子结构，速度与质量均优于现有模型。
Scalable Autoregressive 3D Molecule Generation
- 原子逐个生成，结合因果Transformer与小型扩散MLP预测类型和位置。
- 生成质量显著提升，速度比自回归基线快，接近扩散模型性能。
- 无需修改架构即可处理氢原子添加、骨架补全等变长任务。
3D分子结构生成模型在分子设计与模拟中日益重要。当前扩散模型主导该领域，而自回归模型表现较弱。本文提出Quetzal，一种简单且可扩展的自回归模型，以原子序列为单位逐步构建3D分子。模型使用因果Transformer预测下一个原子的离散类型，同时采用小型扩散MLP建模连续位置分布。相比现有自回归基线，Quetzal在生成质量上实现显著提升，并达到与先进扩散模型相当的性能。此外，通过减少密集Transformer的前向传播次数，Quetzal大幅提高生成速度，并支持基于发散的精确似然计算。更重要的是，无需架构调整，Quetzal天然支持氢原子添加、骨架补全等变长任务。本工作希望推动3D分子生成建模在可扩展性与通用性方面的新视角。
原文摘要 · Abstract (English)
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.
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