Predicting the docking between proteins and ligands is a crucial and
challenging task for drug discovery. However, traditional docking methods
mainly rely on scoring functions, and deep learning-based docking approaches
usually neglect the 3D spatial information of proteins and ligands, as well as
the graph-level features of ligands, which limits their performance. To address
these limitations, we propose an equivariant transformer neural network for
protein-ligand docking pose prediction. Our approach involves the fusion of
ligand graph-level features by feature processing, followed by the learning of
ligand and protein representations using our proposed TAMformer module.
Additionally, we employ an iterative optimization approach based on the
predicted distance matrix to generate refined ligand poses. The experimental
results on real datasets show that our model can achieve state-of-the-art
performance.

应用等变性转换神经网络模型，利用蛋白质和配体的 3D 空间信息以及配体的图级特征进行融合和学习，实现了蛋白质 - 配体对接位姿的预测，并通过迭代优化生成精炼的配体位姿，实验证明该模型具有最先进的性能。

ETDock: 一种新颖的蛋白质 - 配体对接等变换 Transformer 模型

ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking

While most network embedding techniques model the relative positions of nodes
in a network, recently there has been significant interest in structural
embeddings that model node role equivalences, irrespective of their distances
to any specific nodes. We present PhUSION, a proximity-based unified framework
for computing structural and positional node embeddings, which leverages
well-established methods for calculating node proximity scores. Clarifying a
point of contention in the literature, we show which step of PhUSION produces
the different kinds of embeddings and what steps can be used by both. Moreover,
by aggregating the PhUSION node embeddings, we obtain graph-level features that
model information lost by previous graph feature learning and kernel methods.
In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35
methods, we systematically reveal successful design choices for node and
graph-level machine learning with embeddings.

本篇论文介绍了 PhUSION，这是一种用于计算结构和位置节点嵌入的基于接近度的统一框架，通过聚合节点嵌入，得到模拟先前的图特征学习和核方法丢失信息的图层次特征，主要适用于节点和图层次的机器学习。