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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 611–620 of 691 papers

TitleStatusHype
Learning Invariances in Neural Networks from Training Data—0
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
TrimNet: learning molecular representation from triplet messages for biomedicineCode1
Polymer Informatics: Current Status and Critical Next Steps—0
Controlled Molecule Generator for Optimizing Multiple Chemical PropertiesCode0
Learning Invariances in Neural NetworksCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties—0
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