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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 661–670 of 691 papers

TitleStatusHype
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
Molecule Property Prediction Based on Spatial Graph EmbeddingCode0
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling PerspectiveCode0
testRNN: Coverage-guided Testing on Recurrent Neural NetworksCode0
Artificial Intelligence Enabled Material Behavior Prediction—0
Path-Augmented Graph Transformer NetworkCode0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorsCode0
DScribe: Library of Descriptors for Machine Learning in Materials ScienceCode0
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