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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 181–190 of 691 papers

TitleStatusHype
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
MEG: Generating Molecular Counterfactual Explanations for Deep Graph NetworksCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Assigning Confidence to Molecular Property PredictionCode1
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
Molecular machine learning with conformer ensemblesCode1
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug DiscoveryCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
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