Message passing neural networks have demonstrated significant efficacy in
predicting molecular interactions. Introducing equivariant vectorial
representations augments expressivity by capturing geometric data symmetries,
thereby improving model accuracy. However, two-body bond vectors in opposition
may cancel each other out during message passing, leading to the loss of
directional information on their shared node. In this study, we develop
Equivariant N-body Interaction Networks (ENINet) that explicitly integrates
equivariant many-body interactions to preserve directional information in the
message passing scheme. Experiments indicate that integrating many-body
equivariant representations enhances prediction accuracy across diverse scalar
and tensorial quantum chemical properties. Ablation studies show an average
performance improvement of 7.9% across 11 out of 12 properties in QM9, 27.9% in
forces in MD17, and 11.3% in polarizabilities (CCSD) in QM7b.

通过引入等变向量表示，消息传递神经网络在预测分子相互作用方面表现出显著的功效。在消息传递过程中，两个相对的化学键向量可能会相互抵消，导致他们共享节点上的方向信息丢失。本研究开发了等变 N 体互作用网络（ENINet），明确地整合等变的多体互作用以保留消息传递方案中的方向信息。实验证明，整合多体等变表示增强了对不同标量和张量量子化学性质的预测准确性。消融研究显示，在 QM9 的 12 个性质中有 11 个平均性能提升了 7.9%，在 MD17 的力中提升了 27.9%，在 QM7b 的极化率（CCSD）中提升了 11.3%。

分子图网络具有多体等变相互作用

Molecule Graph Networks with Many-body Equivariant Interactions

Molecular dynamic simulations are important in computational physics,
chemistry, material, and biology. Machine learning-based methods have shown
strong abilities in predicting molecular energy and properties and are much
faster than DFT calculations. Molecular energy is at least related to atoms,
bonds, bond angles, torsion angles, and nonbonding atom pairs. Previous
Transformer models only use atoms as inputs which lack explicit modeling of the
aforementioned factors. To alleviate this limitation, we propose Moleformer, a
novel Transformer architecture that takes nodes (atoms) and edges (bonds and
nonbonding atom pairs) as inputs and models the interactions among them using
rotational and translational invariant geometry-aware spatial encoding.
Proposed spatial encoding calculates relative position information including
distances and angles among nodes and edges. We benchmark Moleformer on OC20 and
QM9 datasets, and our model achieves state-of-the-art on the initial state to
relaxed energy prediction of OC20 and is very competitive in QM9 on predicting
quantum chemical properties compared to other Transformer and Graph Neural
Network (GNN) methods which proves the effectiveness of the proposed
geometry-aware spatial encoding in Moleformer.

我们提出了一种新颖的 Transformer 架构 Moleformer，它将节点（原子）和边（键和非键原子对）作为输入，并使用旋转不变性和平移不变性的几何感知空间编码来模拟它们之间的相互作用。我们在 OC20 和 QM9 数据集上进行了基准测试，Moleformer 在 OC20 的初始状态到松弛能量预测方面实现了最先进的水平，并且在预测量子化学性质方面与其他 Transformer 和图神经网络方法相比非常有竞争力，证明了所提出的几何感知空间编码在 Moleformer 中的有效性。

基于分子几何的 Transformer 模型，用于准确建模三维原子系统

Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling

Molecular property prediction is gaining increasing attention due to its
diverse applications. One task of particular interests and importance is to
predict quantum chemical properties without 3D equilibrium structures. This is
practically favorable since obtaining 3D equilibrium structures requires
extremely expensive calculations. In this work, we design a deep graph neural
network to predict quantum properties by directly learning from 2D molecular
graphs. In addition, we propose a 3D graph neural network to learn from
low-cost conformer sets, which can be obtained with open-source tools using an
affordable budget. We employ our methods to participate in the 2021 KDD Cup on
OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy
gap of molecules. Final evaluation results reveal that we are one of the
winners with a mean absolute error of 0.1235 on the holdout test set. Our
implementation is available as part of the MoleculeX package
(this https URL).

本研究使用基于深度图神经网络的方法，通过从二维分子图中直接学习，以及通过使用低成本构象集合，预测量子化学性质中的 HOMO-LUMO 能隙，并在 2021 KDD Cup 中表现出良好性能。