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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 641–650 of 691 papers

TitleStatusHype
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Uncertainty Quantification Using Neural Networks for Molecular Property PredictionCode1
Multi-View Graph Neural Networks for Molecular Property Prediction—0
Adaptive Invariance for Molecule Property Prediction—0
Continuous Representation of Molecules Using Graph Variational Autoencoder—0
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks—0
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property PredictionCode1
Neural Message Passing on High Order Paths—0
Molecule Attention TransformerCode1
Molecule Property Prediction and Classification with Graph HypernetworksCode0
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