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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 561570 of 691 papers

TitleStatusHype
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction0
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Multi-task Learning with Domain Knowledge for Molecular Property Prediction0
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Optimal Decision Making in High-Throughput Virtual Screening Pipelines0
Chemical-Reaction-Aware Molecule Representation LearningCode1
Prediction of properties of metal alloy materials based on machine learning0
Multilingual Molecular Representation Learning via Contrastive Pre-training0
Generative Pre-Training from MoleculesCode1
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art0
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