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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 651–660 of 691 papers

TitleStatusHype
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
Neural Message Passing on High Order Paths—0
Molecule Property Prediction and Classification with Graph HypernetworksCode0
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
Explanatory Masks for Neural Network Interpretability—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Towards Interpretable Molecular Graph Representation Learning—0
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
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