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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 431–440 of 691 papers

TitleStatusHype
Pre-training of Molecular GNNs via Conditional Boltzmann Generator—0
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Enhancing Molecular Property Prediction via Mixture of Collaborative ExpertsCode0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures—0
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks—0
Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine LearningCode0
Multiparameter Persistent Homology for Molecular Property Prediction—0
Unsupervised Musical Object Discovery from AudioCode0
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