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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 131140 of 691 papers

TitleStatusHype
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Assigning Confidence to Molecular Property PredictionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
3DReact: Geometric deep learning for chemical reactionsCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
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