We present a new model for generating molecular data by combining discrete
and continuous diffusion processes. Our model generates a comprehensive
representation of molecules, including atom features, 2D discrete molecule
structures, and 3D continuous molecule coordinates. The use of diffusion
processes allows for capturing the probabilistic nature of molecular processes
and the ability to explore the effect of different factors on molecular
structures and properties. Additionally, we propose a novel graph transformer
architecture to denoise the diffusion process. The transformer is equivariant
to Euclidean transformations, allowing it to learn invariant atom and edge
representations while preserving the equivariance of atom coordinates. This
transformer can be used to learn molecular representations robust to geometric
transformations. We evaluate the performance of our model through experiments
and comparisons with existing methods, showing its ability to generate more
stable and valid molecules with good properties. Our model is a promising
approach for designing molecules with desired properties and can be applied to
a wide range of tasks in molecular modeling.

本文提出一种新的分子数据生成模型，它将离散与连续的扩散过程相结合。 通过扩散过程的使用，可以捕获分子过程的概率性质，并探索不同因素对分子结构和性质的影响。 此外，文中还提出了一种新颖的图形变换器架构，用于去噪扩散过程，可以用于学习鲁棒的分子表示。 通过实验和与现有方法的比较，表明该模型能够生成更稳定和有效的分子，并具有良好的性能。 该模型为设计具有所需特性的分子提供了一种有前途的方法，并可应用于分子建模的广泛任务。

MUDiff：完整分子生成的统一扩散

MUDiff: Unified Diffusion for Complete Molecule Generation

Spatial and time-dependent data is of interest in many applications. This
task is difficult due to its complex spatial dependency, long-range temporal
dependency, data non-stationarity, and data heterogeneity. To address these
challenges, we propose Forecaster, a graph Transformer architecture.
Specifically, we start by learning the structure of the graph that
parsimoniously represents the spatial dependency between the data at different
locations. Based on the topology of the graph, we sparsify the Transformer to
account for the strength of spatial dependency, long-range temporal dependency,
data non-stationarity, and data heterogeneity. We evaluate Forecaster in the
problem of forecasting taxi ride-hailing demand and show that our proposed
architecture significantly outperforms the state-of-the-art baselines.

通过基于图的 Transformer 模型 Forecaster 来解决时空相关数据处理中的空间依赖、时间依赖、数据非平稳性和数据异质性问题，并在预测出租车叫车需求问题中取得了显著的优势。