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 of computer-aided drug design. The course will sensitize the students 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, we will perform a small task from the described computer-aided drug design pipeline (morning lecture followed by practical work asn presentation). The students can work individually or in groups, depending on the total number of participants.