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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
Equivariance Everywhere All At Once: A Recipe for Graph Foundation 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
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
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