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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 161170 of 691 papers

TitleStatusHype
Why Propagate Alone? Parallel Use of Labels and Features on GraphsCode1
Relative Molecule Self-Attention TransformerCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
Generative Pre-Training from MoleculesCode1
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