论文标题
Visnet：具有矢量 - 量表互动消息传递分子的等效几何增强图神经网络
ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
论文作者
论文摘要
几何深度学习一直在彻底改变分子建模场。尽管最先进的神经网络模型正在从头开始对分子财产预测的准确性，但由于几何信息的利用不足和高度计算成本的利用不足，它们的应用，例如药物发现和分子动力学（MD）模拟。在这里，我们提出了一个名为Visnet的模棱两可的几何形状增强图神经网络，它优雅地提取了几何特征，并有效地模拟了计算成本低的分子结构。我们提出的Visnet在包括MD17，修订后的MD17和MD22在内的多个MD基准上的最先进方法优于最先进的方法，并在QM9和Molecule3D数据集上实现了出色的化学性质预测。此外，Visnet在OGB-LCS@Neurips2022比赛中获得了PCQM4MV2曲目的最高冠军。此外，通过一系列的模拟和案例研究，Visnet可以有效探索构象空间，并为将几何表示形式映射到分子结构。
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.
