Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes

• URL: http://arxiv.org/abs/2206.02789v3
• Date: Sun, 19 Nov 2023 04:38:51 GMT
• Title: Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
• Authors: Shuo Zhang, Yang Liu, Lei Xie
• Abstract summary: We propose Physics-aware Multiplex Graph Neural Network (PaxNet) to efficiently learn the representations of 3D molecules. PaxNet separates the modeling of local and non-local interactions inspired by molecular mechanics. It can also predict vectorial properties by learning an associated vector for each atom.
• Score: 19.268713909099507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However, most existing GNNs suffer from the limitations of insufficient modeling of diverse interactions, computational expensive operations, and ignorance of vectorial values. Here, we tackle these limitations by proposing a novel GNN model, Physics-aware Multiplex Graph Neural Network (PaxNet), to efficiently and accurately learn the representations of 3D molecules for both small organic compounds and macromolecule complexes. PaxNet separates the modeling of local and non-local interactions inspired by molecular mechanics, and reduces the expensive angle-related computations. Besides scalar properties, PaxNet can also predict vectorial properties by learning an associated vector for each atom. To evaluate the performance of PaxNet, we compare it with state-of-the-art baselines in two tasks. On small molecule dataset for predicting quantum chemical properties, PaxNet reduces the prediction error by 15% and uses 73% less memory than the best baseline. On macromolecule dataset for predicting protein-ligand binding affinities, PaxNet outperforms the best baseline while reducing the memory consumption by 33% and the inference time by 85%. Thus, PaxNet provides a universal, robust and accurate method for large-scale machine learning of molecules. Our code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN.

Related papers

• A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems [19.268713909099507]
  PAMNet is a universal framework for learning the representations of 3D molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. In benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks.
  arXiv  Detail & Related papers  (2023-11-19T04:52:05Z)
• Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel [48.6168145845412]
  We propose a method to abstract the collective information of atomic groups into a few $textitNeural Atoms$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop.
  arXiv  Detail & Related papers  (2023-11-02T14:44:50Z)
• ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules [69.05950120497221]
  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.
  arXiv  Detail & Related papers  (2022-10-29T07:12:46Z)
• Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction [20.276492931562036]
  Given the limited number of experimentally determined RNA structures, the prediction of RNA structures will facilitate elucidating RNA functions and RNA-targeted drug discovery. We propose a Graph Neural Network (GNN)-based scoring function trained only with the atomic types. The proposed Physics-aware Multiplex Graph Neural Network (PaxNet) separately models the local and non-local interactions.
  arXiv  Detail & Related papers  (2022-10-28T20:13:00Z)
• MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras [51.92255321684027]
  MolGraph is a graph neural network (GNN) package for molecular machine learning (ML) MolGraph implements a chemistry module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data.
  arXiv  Detail & Related papers  (2022-08-21T18:37:41Z)
• 3D Infomax improves GNNs for Molecular Property Prediction [1.9703625025720701]
  We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. We show that 3D pre-training provides significant improvements for a wide range of properties.
  arXiv  Detail & Related papers  (2021-10-08T13:30:49Z)
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs [79.06686274377009]
  We develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Our method can achieve comparable prediction accuracy but with much smaller computational costs.
  arXiv  Detail & Related papers  (2021-09-30T22:09:28Z)
• 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)
• Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction [11.726245297344418]
  Kernel method and graph neural networks have been widely studied as two mainstream methods for property prediction. In this work, we shed light on the Directed Graph Attention Neural Network (DGANN), which only takes chemical bonds as edges. Our model has matched or outperformed most baseline graph neural networks on QM9 datasets.
  arXiv  Detail & Related papers  (2020-12-01T11:06:40Z)
• Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures [20.276492931562036]
  A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. We build Multiplex Molecular Graph Neural Network (MXMNet)
  arXiv  Detail & Related papers  (2020-11-15T05:55:15Z)
• Deep Polynomial Neural Networks [77.70761658507507]
  $Pi$Nets are a new class of function approximators based on expansions. $Pi$Nets produce state-the-art results in three challenging tasks, i.e. image generation, face verification and 3D mesh representation learning.
  arXiv  Detail & Related papers  (2020-06-20T16:23:32Z)

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.