Big Data in Drug Discovery and Development
Full course description
This course provides an in-depth insight how to exploit information publicly available in multiple web-based data infrastructures and how to use different software tools for drug discovery, design and further development. It will provide an introduction to how drugs can be designed using tools that can be applied for docking of potential molecular drug structures to protein targets, computerized tools that can be used to calculate properties of drugs (e.g. logP, Molecular Weight, Lipinski Parameters, etc.) and abstracted bioactivities (e.g. binding constants, pharmacology and ADMET). It will also provide insight how to use genomics data for complementing drug structure-activity relationships, including data retrieved from patients, which can be applied for identifying potential targets of drugs. The course also encompasses practical training in using these different in silico tools, which will be used to gather information about potential drugs and of existing drugs.
Students will be able to understand:
- biomarker discovery, exploring mechanisms, use of omics approaches;
- in-silico modelling, computerized drug-protein interactions and activities;
- training how to use different databases, eTox, ChEMBL, Open Phacts, Open TG-GATEs, diXa, as well as relevant software tools;
- skills: Computer supported Training in Big Data in Drug Discovery & Development;
- biology underlying fundamental psychological processes.