In this work, we introduce AutoFragDiff, a fragment-based autoregressive
diffusion model for generating 3D molecular structures conditioned on target
protein structures. We employ geometric vector perceptrons to predict atom
types and spatial coordinates of new molecular fragments conditioned on
molecular scaffolds and protein pockets. Our approach improves the local
geometry of the resulting 3D molecules while maintaining high predicted binding
affinity to protein targets. The model can also perform scaffold extension from
user-provided starting molecular scaffold.

该论文介绍了 AutoFragDiff，一种基于片段的自回归扩散模型，用于生成以目标蛋白质结构为条件的 3D 分子结构。我们使用几何矢量感知器预测新的分子片段的原子类型和空间坐标，以分子支架和蛋白质口袋为条件。我们的方法改善了生成的 3D 分子的局部几何性质，同时保持了对蛋白质靶标的高预测结合亲和力。该模型还能够从用户提供的起始分子支架进行扩展。

基于自回归片段的扩散策略在口袋感知的配体设计中的应用

Autoregressive fragment-based diffusion for pocket-aware ligand design

In this paper, we introduce a novel generative model, Diffusion Layout
Transformers without Autoencoder (Dolfin), which significantly improves the
modeling capability with reduced complexity compared to existing methods.
Dolfin employs a Transformer-based diffusion process to model layout
generation. In addition to an efficient bi-directional (non-causal joint)
sequence representation, we further propose an autoregressive diffusion model
(Dolfin-AR) that is especially adept at capturing rich semantic correlations
for the neighboring objects, such as alignment, size, and overlap. When
evaluated against standard generative layout benchmarks, Dolfin notably
improves performance across various metrics (fid, alignment, overlap, MaxIoU
and DocSim scores), enhancing transparency and interoperability in the process.
Moreover, Dolfin's applications extend beyond layout generation, making it
suitable for modeling geometric structures, such as line segments. Our
experiments present both qualitative and quantitative results to demonstrate
the advantages of Dolfin.

该论文介绍了一种新颖的生成模型 —— 无自动编码器的扩散布局变换器 (Dolfin)，与现有方法相比，该模型的建模能力得到显著提升，复杂度减少。Dolfin 采用基于 Transformer 的扩散过程来建模布局生成，同时提出了一种自回归扩散模型 (Dolfin-AR)，特别擅长捕捉邻近对象的丰富语义相关性，如对齐、大小和重叠。在标准的生成布局基准测试中，Dolfin 在各种指标 (fid、对齐度、重叠度、MaxIoU 和 DocSim 得分) 上显著提高了性能，增强了透明度和互操作性。此外，Dolfin 的应用不仅局限于布局生成，还适用于建模几何结构，如线段。我们的实验证明了 Dolfin 的优势，展示了定性和定量结果。

Dolfin: 无自编码器的扩散布局变换器

Dolfin: Diffusion Layout Transformers without Autoencoder

Diffusion-based graph generative models have recently obtained promising
results for graph generation. However, existing diffusion-based graph
generative models are mostly one-shot generative models that apply Gaussian
diffusion in the dequantized adjacency matrix space. Such a strategy can suffer
from difficulty in model training, slow sampling speed, and incapability of
incorporating constraints. We propose an \emph{autoregressive diffusion} model
for graph generation. Unlike existing methods, we define a node-absorbing
diffusion process that operates directly in the discrete graph space. For
forward diffusion, we design a \emph{diffusion ordering network}, which learns
a data-dependent node absorbing ordering from graph topology. For reverse
generation, we design a \emph{denoising network} that uses the reverse node
ordering to efficiently reconstruct the graph by predicting the node type of
the new node and its edges with previously denoised nodes at a time. Based on
the permutation invariance of graph, we show that the two networks can be
jointly trained by optimizing a simple lower bound of data likelihood. Our
experiments on six diverse generic graph datasets and two molecule datasets
show that our model achieves better or comparable generation performance with
previous state-of-the-art, and meanwhile enjoys fast generation speed.

我们提出了一种基于扩散的图生成模型，通过定义在离散图空间中操作的节点吸收扩散过程，设计了扩散排序网络和去噪网络，从而实现了更好或相当的生成性能，并且具有快速的生成速度。