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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
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
testRNN: Coverage-guided Testing on Recurrent Neural NetworksCode0
Embedding Graphs on Grassmann ManifoldCode0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Conditional molecular design with deep generative modelsCode0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningCode0
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point CloudsCode0
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