Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction

@article{Qian2020DirectedGA,
  title={Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction},
  author={Chen Qian and Yunhai Xiong and Xiang Chen},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.00404}
}
The prosperity of computer vision (CV) and natural language procession (NLP) in recent years has spurred the development of deep learning in many other domains. The advancement in machine learning provides us with an alternative option besides the computationally expensive density functional theories (DFT). Kernel method and graph neural networks have been widely studied as two mainstream methods for property prediction. The promising graph neural networks have achieved comparable accuracy to… 
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SHOWING 1-10 OF 32 REFERENCES
Analyzing Learned Molecular Representations for Property Prediction
TLDR
A graph convolutional model is introduced that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets.
Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures.
TLDR
This study proposes two novel models for aqueous solubility predictions, based on the Multilevel Graph Convolutional Network (MGCN) and SchNet architectures, respectively, and found that both the MGCN and Sch net models performed well for aQueous solUBility predictions.
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.
TLDR
A crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
TLDR
This work develops, for the first time, universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals and demonstrates the transfer learning of elemental embeddings from a property model trained on a larger data set to accelerate the training of property models with smaller amounts of data.
SchNet - A deep learning architecture for molecules and materials.
TLDR
The deep learning architecture SchNet is presented that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers and employs SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules.
Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation
TLDR
Light is shed on a high potential of machine learning (ML)-based approach for automated analysis of atomistic configuration since it is not straightforward to develop an identifier of atomic configuration manually when the authors face a new problem out of existing methodologies.
Deep neural networks for accurate predictions of crystal stability
TLDR
A deep learning approach is developed which, just using two descriptors, provides crystalline formation energies with very high accuracy, addressing a critical gap in the extension of machine-learning models from fixed stoichiometry crystals to infinite universe of mixed-species crystals.
Hierarchical modeling of molecular energies using a deep neural network.
TLDR
HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error.
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.
TLDR
PhysNet is introduced, a DNN architecture designed for predicting energies, forces, and dipole moments of chemical systems, and it is shown that explicitly including electrostatics in energy predictions is crucial for a qualitatively correct description of the asymptotic regions of a potential energy surface (PES).
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
TLDR
Numerical evidence that ML model predictions deviate from DFT less than DFT (B3LYP) deviates from experiment for all properties is presented and suggests that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available.
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