Teaching

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Teaching

1.   Lab Seminar: Messung und Analyse Physiologischer Prozesse: Using open source software and libraries for data analysis and computer-aided drug design

•    Date: 27.03.-31.03.2017, 5 days, daily 9 am - 5 pm
•    Location: Campus Charité Mitte

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 (CADD).  The course will sensitize the student to the ease and the profit of using bio-/cheminformatics methods.
The course shell enable the students to set-up their own drug design pipeline including 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 used 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 in groups or individually, depending on the total number of participants.

2.    Projektmanagement (Softwarepraktikum) SoSe2017

Project H: Platform for ligand-based toxicity prediction

•    Date: tba (flexible), 8 weeks duration
•    Participants: 4
•    Location: Campus Charité Mitte

The in-silico prediction of compound toxicity is a very important task, i.e. for the reduction of animal testing (BB3R). Thus, we will implement Python scripts to set up a machine learning model for toxicity predictions. The data from freely available sources for toxicity predictions will be collected. This test and training data together with different molecular descriptors will be the basis for our toxicity predictor. Besides exhaustive evaluation of the models, a new method will be developed to extract the molecular features responsible for the toxic effect.
If successful, the model will be made available as a user-friendly web-server to allow other researchers to predict the toxicity of their compound of interest. A potential publication would be the ultimate goal of our work.
Used programming languages: Python (scikit-learn, RDKit), Webserver (django, json, jquery, …)

slides from presentation 20.01.2017

3.    Seminar series: Computer-aided drug design – Methods and Application (SoSe 2017)

Introductory seminar to computer-aided drug design, including structure- and ligand-based methods. Briefly, computer-aided drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. In this seminar, the students will independently acquire knowledge about topics in the context of structural bioinformatics. Topics include homology modeling, target assessment, molecular docking, in-silico screening (molecular fingerprints, chemical similarity), ADME and toxicity predictions (QSAR, machine learning), and more.

The students can select a topic from the presented drug development pipeline and (under guidance) will collect material (papers, data and algorithms). At the end of the seminar, they will present the topic as well as an application of such.

Starting date: Thursday, April 20th, 10-12 am, Dahlem