Professional Diploma in Computational Drug Discovery (C3D)

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 drug discovery context. Example topics include protein homology modeling, ligand-protein molecular docking, and other prediction methods. The course meetings embrace a blend of lectures and practical sessions. The coursework includes readings, assignments, and ends with a project presentation by students.

This course introduces fundamental and advanced concepts and methods in structural bioinformatics and ligand-based approaches in drug discovery. 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, proteinprotein interactions and docking, protein-DNA interactions, protein electrostatics and molecular dynamics simulations (MD).

In this course, different types of supervised and unsupervised machine learning methods such asPCA, HCA and ANN, that were exploited in drug and nanoparticles formulation and deliveryaspects and will be demonstrated and discussed. The crossdisciplinary integration of drugdelivery and machine learning methods as a branch of artificial intelligence may shift theparadigm of pharmaceutical research from experience-dependent investigations to datadrivenstudies. Exploitation of these methods in the drug delivery field especially with the support fromthe pharmaceutical industry would lead to huge cuts in the resources expenses and would leadto large savings in efforts and time that are usually exerted in the wet-lab try-and-errorexperiments.

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 the instructors and the candidates. At the end of this course, it is highly encouraged to formulate the outcomes as a publication.

In this course, different types of supervised and unsupervised machine learning methods such as PCA, HCA, and ANN, that were exploited in drug and nanoparticle formulation and delivery aspects will be demonstrated and discussed.

This course introduces the basic and applied aspects of quantum chemistry, which are relevant to present day computational drug discovery and development. Topics covered include the fundamentals of quantum mechanics, the Hartree-Fock approximation, density functional theory, and post-Hartree-Fock methods, atomic basis sets, the calculation of energies, properties, and specroscopic signatures, the calculation of intermolecular interaction energies, the application of the FMO method in structurebased drug design.