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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
Orbital Graph Convolutional Neural Network for Material Property Prediction—0
Deep Learning based Dimple Segmentation for Quantitative Fractography—0
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems—0
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
DeeperGCN: All You Need to Train Deeper GCNsCode0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
Enforcing Predictive Invariance across Structured Biomedical Domains—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Multi-View Graph Neural Networks for Molecular Property Prediction—0
Adaptive Invariance for Molecule Property Prediction—0
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