Practical Course: Using open source software and libraries for data analysis and computer-aided drug design

This practical course introduces the students to diverse software tools and libraries which can support data generation and analysis in various areas such as computer-aided drug design.  The course will sensitize the student to the ease and the profit of using bio-/cheminformatics methods.

The course shall enable the students to set-up their own drug design pipeline including protein target preparation, compound library set-up and filtering (i.e. using drug-like and toxicity filters), docking of the compounds to the target and evaluation of the results. The course will include the following tasks:

• There will be a short introductory part into python programming using the novel and interactive IPython notebooks.
• The students will write their own scripts using, e.g., python libraries for data handling (pandas), protein (BioPython) and compound (RDKit) processing as well as machine learning (scikit-learn) to prepare their compound libraries.  
• Open access tools for protein structure preparation and visual inspection (PyMol) will be used.
• AutoDock software will be engaged for docking of the compounds into the target protein and the results will be evaluated.   

Each day, they will perform a small task from the described computer-aided drug design pipeline. The students can work individually or in groups, depending on the total number of participants.

Practical course date: 27.03.-31.03.2017, 5 days, daily 9 am - 5 pm.
Preliminary meeting: Date will be announced soon.