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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

Showing 191200 of 691 papers

TitleStatusHype
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
TrimNet: learning molecular representation from triplet messages for biomedicineCode1
Learning Invariances in Neural NetworksCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
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
Object-Centric Learning with Slot AttentionCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Wasserstein Embedding for Graph LearningCode1
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