Leveraging graph neural networks for enhanced prediction of molecular solubility via transfer learning
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In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.
Nội dung trích xuất từ tài liệu:
Leveraging graph neural networks for enhanced prediction of molecular solubility via transfer learning
Nội dung trích xuất từ tài liệu:
Leveraging graph neural networks for enhanced prediction of molecular solubility via transfer learning
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Molecular property prediction Machine learning Deep learning Graph neural network Transfer learningGợi ý tài liệu liên quan:
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