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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 81–90 of 691 papers

TitleStatusHype
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative GamesCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Directed Graph Grammars for Sequence-based LearningCode1
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