التفاصيل البيبلوغرافية
| العنوان: |
An intelligence model to forecast blood brain barrier permeability using graph neural network. |
| المؤلفون: |
Dhivyaprabha, T. T., Susan, M. B. Jennyfer, Lalitha, P., Subashini, P., Kamilini, D., Vijayabhanu, R. |
| المصدر: |
AIP Conference Proceedings; 2024, Vol. 3122 Issue 1, p1-10, 10p |
| مصطلحات موضوعية: |
BLOOD-brain barrier, GRAPH neural networks, DEEP learning, PERMEABILITY, MOLECULAR structure, SMALL molecules |
| مستخلص: |
Blood Brain Barrier (BBB) is a membrane made of a network of tissues which protects the brain from noxious agents that could cause brain infections. It only allows small molecules and lipid-soluble molecules to pass through the membrane. Brain drugs need to penetrate the Blood Brain Barrier (BBB), so molecular property prediction is an important process in designing and developing a new drug. Molecular property prediction is a process of predicting the property of a molecule from its molecular structure. Graph Neural Networks (GNNs) are a range of deep learning techniques that can be directly applied to graph data for prediction. Since the structure of the molecule is considered a graph, GNN is applied to predict the molecular property of the molecule. GNN includes two types, Message Passing Neural Networks (MPNN) and Graph Convolutional Networks (GCN). In this paper, the MPNN model and the GCN model are compared to determine the best model suitable for the prediction of BBB permeability. Based on the comparison of the GNN models, the MPNN model outperforms the GCN model with a prediction accuracyof 90%. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
