Advanced generative model (e.g., diffusion model) derived from simplified
continuity assumptions of data distribution, though showing promising progress,
has been difficult to apply directly to geometry generation applications due to
the multi-modality and noise-sensitive nature of molecule geometry. This work
introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits
molecule geometry by modeling diverse modalities in the differentiable
parameter space of distributions. GeoBFN maintains the SE-(3) invariant density
modeling property by incorporating equivariant inter-dependency modeling on
parameters of distributions and unifying the probabilistic modeling of
different modalities. Through optimized training and sampling techniques, we
demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D
molecule generation benchmarks in terms of generation quality (90.87% molecule
stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct
sampling with any number of steps to reach an optimal trade-off between
efficiency and quality (e.g., 20-times speedup without sacrificing
performance).

通过模拟分布参数空间中不同模态的可微参数依赖模型，GeoBFN 是一种局部变换不变密度建模方法，适用于分子几何生成任务，并且在多个三维分子生成基准测试中取得了最先进的性能。

基于贝叶斯流网络的三维分子统一生成建模

Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks

Recently, artificial intelligence for drug discovery has raised increasing
interest in both machine learning and chemistry domains. The fundamental
building block for drug discovery is molecule geometry and thus, the molecule's
geometrical representation is the main bottleneck to better utilize machine
learning techniques for drug discovery. In this work, we propose a pretraining
method for molecule joint auto-encoding (MoleculeJAE). MoleculeJAE can learn
both the 2D bond (topology) and 3D conformation (geometry) information, and a
diffusion process model is applied to mimic the augmented trajectories of such
two modalities, based on which, MoleculeJAE will learn the inherent chemical
structure in a self-supervised manner. Thus, the pretrained geometrical
representation in MoleculeJAE is expected to benefit downstream
geometry-related tasks. Empirically, MoleculeJAE proves its effectiveness by
reaching state-of-the-art performance on 15 out of 20 tasks by comparing it
with 12 competitive baselines.

该研究提出了一种面向分子联合自编码（MoleculeJAE）的预训练方法，该方法可以学习分子的二维键和三维构象信息，通过扩增轨迹的弥散过程模型，MoleculeJAE 能够以自我监督的方式学习分子的化学结构，优于与其相比的 12 个竞争基准，在 20 个任务中有 15 个任务达到了最先进的性能水平。