Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

• URL: http://arxiv.org/abs/2106.08551v1
• Date: Wed, 16 Jun 2021 05:07:28 GMT
• Title: Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
• Authors: Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, and Shuiwang Ji
• Abstract summary: We design a deep graph neural network to predict quantum properties by directly learning from 2D molecular graphs. We also propose a 3D graph neural network to learn from low-coster sets.
• Score: 41.727588601578155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (https://github.com/divelab/MoleculeX).

Related papers

• 3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds [71.39129855825402]
  Existing methods for learning 3D representations are deep neural networks trained and tested on classical hardware. This paper introduces the first quantum auto-encoder for 3D point clouds.
  arXiv  Detail & Related papers  (2023-11-09T18:58:33Z)
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Automated 3D Pre-Training for Molecular Property Prediction [54.15788181794094]
  We propose a novel 3D pre-training framework (dubbed 3D PGT) It pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT.
  arXiv  Detail & Related papers  (2023-06-13T14:43:13Z)
• 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)
• Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes [19.268713909099507]
  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.
  arXiv  Detail & Related papers  (2022-06-06T00:28:37Z)
• 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)
• On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction [0.0]
  The PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on about 3.8M graphs. We show our current work-in-progress solution which builds an ensemble of three graph neural networks models.
  arXiv  Detail & Related papers  (2021-06-29T15:58:34Z)
• 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)

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.