This course provides aspects of structural bioinformatics and deals with computational applications in drug discovery; it starts with a review of protein modeling and then quickly moves into computational techniques with a special emphasis on the drug discovery context. Example topics include protein homology modeling, ligand-protein molecular docking, and other prediction methods. The course

Topics covered include ligand-based approaches such as, molecular simulation and data set construction, quantitative structure-activity relationships (QSAR), pharmacophore modeling and target phishing approaches. Furthermore, the course provides a focus on practical applications of advanced structural bioinformatics, such as, protein-protein interactions and docking, protein-DNA interactions

The use of computational methods in drug discovery and development has become a well-established practice. Many students nowadays are familiar with terms like molecular docking, pharmacophore search, QSAR, molecular dynamics, and virtual screening. Because of the complexity of the underlying molecular phenomena, and the priority of speed over accuracy in high throughput screening, all of these

This course can be considered as a project (case study) with a major practical application for a topic of the previously described courses. Furthermore, topics are not taught in the curriculum are also welcome, such as fragment-based approaches, vaccine design, antibody design, protein design, deep docking, big dataset screening…etc. Any topic can also be considered based on mutual agreement with

This course includes but not limited to the following main topics: The applications of machine learning as a part of artificial intelligence in drug development and delivery. In this course, different types of supervised and un-supervised machine learning methods such as PCA, HCA and ANN, that were exploited in drug and nanoparticles formulation and delivery aspects and will be demonstrated and