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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 571–580 of 691 papers

TitleStatusHype
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Hierarchical graph neural nets can capture long-range interactionsCode1
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks—0
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
Generalization and Robustness Implications in Object-Centric LearningCode1
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation LearningCode0
Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
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