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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 621–630 of 691 papers

TitleStatusHype
Gaussian Process Molecule Property Prediction with FlowMO—0
Improving Graph Property Prediction with Generalized Readout FunctionsCode0
Chemical Property Prediction Under Experimental Biases—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
Graph Neural Network Architecture Search for Molecular Property Prediction—0
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
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