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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 111120 of 691 papers

TitleStatusHype
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Learning to Split for Automatic Bias DetectionCode1
E(n) Equivariant Topological Neural NetworksCode1
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
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