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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 601–610 of 691 papers

TitleStatusHype
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Polymer Informatics: Current Status and Critical Next Steps—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
PotentialNet for Molecular Property Prediction—0
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution—0
Prediction of properties of metal alloy materials based on machine learning—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
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