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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 621630 of 691 papers

TitleStatusHype
Towards Faster and More Compact Foundation Models for Molecular Property PredictionCode0
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine LearningCode0
Object-Centric Learning with Slot Mixture ModuleCode0
Improving Molecular Pretraining with Complementary FeaturizationsCode0
An open unified deep graph learning framework for discovering drug leadsCode0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder LayersCode0
Improving Graph Property Prediction with Generalized Readout FunctionsCode0
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation LearningCode0
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