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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 281–290 of 691 papers

TitleStatusHype
Pre-training of Molecular GNNs via Conditional Boltzmann Generator—0
Molecular Hypergraph Neural NetworksCode1
Graph Transformers for Large GraphsCode1
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
3DReact: Geometric deep learning for chemical reactionsCode1
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
Enhancing Molecular Property Prediction via Mixture of Collaborative ExpertsCode0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Removing Biases from Molecular Representations via Information MaximizationCode1
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