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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 531540 of 691 papers

TitleStatusHype
Improving VAE based molecular representations for compound property predictionCode1
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing0
Pairwise Learning for Neural Link PredictionCode1
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing0
Molecular Contrastive Learning with Chemical Element Knowledge GraphCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
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