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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 631–640 of 691 papers

TitleStatusHype
Learning Invariances in Neural Networks from Training Data—0
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction—0
Polymer Informatics: Current Status and Critical Next Steps—0
Controlled Molecule Generator for Optimizing Multiple Chemical PropertiesCode0
Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties—0
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
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