We introduce ProteinWorkshop, a comprehensive benchmark suite for
representation learning on protein structures with Geometric Graph Neural
Networks. We consider large-scale pre-training and downstream tasks on both
experimental and predicted structures to enable the systematic evaluation of
the quality of the learned structural representation and their usefulness in
capturing functional relationships for downstream tasks. We find that: (1)
large-scale pretraining on AlphaFold structures and auxiliary tasks
consistently improve the performance of both rotation-invariant and equivariant
GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a
greater extent compared to invariant models. We aim to establish a common
ground for the machine learning and computational biology communities to
rigorously compare and advance protein structure representation learning. Our
open-source codebase reduces the barrier to entry for working with large
protein structure datasets by providing: (1) storage-efficient dataloaders for
large-scale structural databases including AlphaFoldDB and ESM Atlas, as well
as (2) utilities for constructing new tasks from the entire PDB.
ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.

ProteinWorkshop 是一套用于蛋白质结构上的几何图神经网络表征学习的全面基准套件，通过对大规模预训练和下游任务进行系统评估，旨在建立机器学习和计算生物学社区之间的共同基础，以严谨地比较和推进蛋白质结构表征学习的研究。

评估蛋白质结构宇宙中的表示学习

Evaluating representation learning on the protein structure universe

By conceiving physical systems as 3D many-body point clouds, geometric graph
neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased
promising performance. In particular, their effective message-passing mechanics
make them adept at modeling molecules and crystalline materials. However,
current geometric GNNs only offer a mean-field approximation of the many-body
system, encapsulated within two-body message passing, thus falling short in
capturing intricate relationships within these geometric graphs. To address
this limitation, tensor networks, widely employed by computational physics to
handle manybody systems using high-order tensors, have been introduced.
Nevertheless, integrating these tensorized networks into the message-passing
framework of GNNs faces scalability and symmetry conservation (e.g.,
permutation and rotation) challenges. In response, we introduce an innovative
equivariant Matrix Product State (MPS)-based message-passing strategy, through
achieving an efficient implementation of the tensor contraction operation. Our
method effectively models complex many-body relationships, suppressing
mean-field approximations, and captures symmetries within geometric graphs.
Importantly, it seamlessly replaces the standard message-passing and
layer-aggregation modules intrinsic to geometric GNNs. We empirically validate
the superior accuracy of our approach on benchmark tasks, including predicting
classical Newton systems and quantum tensor Hamiltonian matrices. To our
knowledge, our approach represents the inaugural utilization of parameterized
geometric tensor networks.

通过创造物理系统的 3D 多体点云，我们提出了一种新型的基于等变矩阵乘积态 (MPS) 的消息传递策略，有效地建模复杂的多体关系并捕捉了几何图中的对称性，超越了现有的几何图神经网络的平均场近似，并在预测经典牛顿系统和量子张量哈密顿矩阵等基准任务上验证了其卓越的准确性，堪称参数化几何张量网络的创新应用。

量子启发式的几何建模的神经网络

A quatum inspired neural network for geometric modeling

Recent advances in computational modelling of atomic systems, spanning
molecules, proteins, and materials, represent them as geometric graphs with
atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric
attributes transform according to the inherent physical symmetries of 3D atomic
systems, including rotations and translations in Euclidean space, as well as
node permutations. In recent years, Geometric Graph Neural Networks have
emerged as the preferred machine learning architecture powering applications
ranging from protein structure prediction to molecular simulations and material
generation. Their specificity lies in the inductive biases they leverage --
such as physical symmetries and chemical properties -- to learn informative
representations of these geometric graphs. In this opinionated paper, we
provide a comprehensive and self-contained overview of the field of Geometric
GNNs for 3D atomic systems. We cover fundamental background material and
introduce a pedagogical taxonomy of Geometric GNN architectures:(1) invariant
networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks
in spherical basis, and (4) unconstrained networks. Additionally, we outline
key datasets and application areas and suggest future research directions. The
objective of this work is to present a structured perspective on the field,
making it accessible to newcomers and aiding practitioners in gaining an
intuition for its mathematical abstractions.

计算模型，几何图，几何图神经网络，三维原子系统，机器学习架构