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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 151160 of 691 papers

TitleStatusHype
A community-powered search of machine learning strategy space to find NMR property prediction modelsCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Dual-view Molecule Pre-trainingCode1
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityCode1
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