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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

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Showing 551–560 of 691 papers

TitleStatusHype
Uni-Mol: A Universal 3D Molecular Representation Learning Framework—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Path-aware Siamese Graph Neural Network for Link PredictionCode0
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction—0
Graph neural networks for the prediction of molecular structure-property relationships—0
Uncertainty quantification for predictions of atomistic neural networksCode0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
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