Deep Learning for Protein Active Site Prediction from Protein Structure
| Autor: | Wei Te Lin, 魏得霖 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Understanding protein is the direction that biologists and pharmacologists have been working on for many years. Protein is made up of many macromolecules and small molecules. Due to the complex structure and interaction of proteins, it has many different functions. Drug design is also based on the studying of proteins and drug ligands binding. The docking position(active site) is special structure of the protein that ligand binding to. This thesis is to design a system for assisted drug design that due to predict active site. Since the rise of deep learning in the field of artificial intelligence, many studies have also begun to experiment with deep learning architectures, which include many predictive studies. The architecture of neural networks used in this paper is recurrent neural networks(RNN) with long-term and short-term memory(LSTM) which is one method of deep learning. This architecture improves the shortcomings of previous recurrent neural networks, allowing the recurrent neural network to have deeper training. The final experimental results will be used to evaluate the accuracy of the active sites predicted by the system. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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