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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 191200 of 691 papers

TitleStatusHype
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Interactive Molecular Discovery with Natural LanguageCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
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