ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules

• URL: http://arxiv.org/abs/2210.16518v3
• Date: Wed, 16 Aug 2023 08:21:22 GMT
• Title: ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
• Authors: Yusong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, Tie-Yan Liu and Tong Wang
• Abstract summary: We propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures. 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.
• Score: 69.05950120497221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• geom2vec: pretrained GNNs as geometric featurizers for conformational dynamics [0.0]
  We introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal featurizers. We learn transferable structural representations that capture molecular geometric patterns without further fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T00:36:06Z)
• Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs [66.98487644676906]
  We introduce Neural P$3$M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities. It exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures.
  arXiv  Detail & Related papers  (2024-09-26T08:16:59Z)
• 3D Molecular Geometry Analysis with 2D Graphs [79.47097907673877]
  Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. We propose a novel deep learning framework to predict 3D geometries from molecular graphs.
  arXiv  Detail & Related papers  (2023-05-01T19:00:46Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer 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.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks [0.0]
  We explore different machine learning (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity.
  arXiv  Detail & Related papers  (2022-01-02T23:00:30Z)
• Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks [14.388196138756195]
  We introduce a graph neural network (GNN) based model, MDNet, to predict the evolution of coordinates and momentum with large time steps. We demonstrate the performance of MDNet on a 4000-atom system with large time steps, and show that MDNet can predict good equilibrium and transport properties.
  arXiv  Detail & Related papers  (2021-11-30T07:32:39Z)
• Heterogeneous relational message passing networks for molecular dynamics simulations [10.30351315399729]
  We propose HermNet, an end-to-end heterogeneous graph neural networks, to efficiently express multiple interactions in a single model. HermNet performs impressively against many top-performing models on both molecular and extended systems.
  arXiv  Detail & Related papers  (2021-09-02T05:04:57Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.