Workshop 5: Machine & Deep Learning Applications in Drug Discovery and Repositioning.
Dr. Meriem Bahi (CRSP)
Abstract:
Due to the time and cost problems with traditional drug discovery, new methods must be found to increase the declining efficiency of traditional approaches. Virtual Screening (VS) is one possible solution to solve this problem. VS of databases has become an attractive method for pharmaceutical research. It plays a crucial role in the early stage of the drug discovery and development process. It aims to reduce the enormous search space for chemical compounds. As the number of ligands in the databases is increasing rapidly, this step should be both fast and effective article/writing to distinguish between active and inactive ligands. Deep learning algorithms can be used for screening big databases of molecules and classifying the ligands as drug-like and non-druglike against a particular protein target and therefore speed up the VS process. On the other hand, discovering potential uses for existing drugs, also known as drug repositioning, is one strategy that has attracted increasing interest from both the pharmaceutical industry and the research community. Discovering new indications for existing drugs can be attained through the identification of new interactions between drugs and target proteins. Currently, experimental methods of identifying new interactions between drugs and targets are cumbersome. In silico machine and deep learning methods, can provide a promising and efficient tool to alleviate this problem, and thus significantly reduce both experimental time and cost of identifying potential drug-target interaction.
Keywords: Drug Discovery, Drug Repositioning, Virtual Screening, Machine Learning, Deep Learning, Drug-Target Interaction, Ligands, Databases.
Outline
1. The Development of a New Drug2. Virtual Screening3. Drug Repositioning4. The Benefits of Drug Repositioning5. Drug Repositioning Strategies6. Prediction of Drug-Target Interactions "DTIs"a. Data Resourcesb. Databasesc. Toolsd. Methods7. Machine Learning8. What is Deep Learning?9. Why Deep Learning?10. Type of Deep Learning Approaches11. Building Deep Networks step by step12. Deep Learning LibrariesWorkshop: Practical Machine and Deep Learning with H2O Platform
1. Installation and Quick-Start
2. Data Import, Data Export
3. Common Model Parameters
4. Building Deep Learning Model
5. Training Deep Learning Model
6. Measurement of Prediction Quality
7. Performance Assessment of the Constructed Model