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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 626–650 of 691 papers

TitleStatusHype
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
Orbital Graph Convolutional Neural Network for Material Property Prediction—0
A community-powered search of machine learning strategy space to find NMR property prediction modelsCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Deep Learning based Dimple Segmentation for Quantitative Fractography—0
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems—0
Object-Centric Learning with Slot AttentionCode1
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Wasserstein Embedding for Graph LearningCode1
DeeperGCN: All You Need to Train Deeper GCNsCode0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Optimal Transport Graph Neural NetworksCode1
Enforcing Predictive Invariance across Structured Biomedical Domains—0
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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