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.

Practical course date: 18.03.-22.03.2019, 5 days, daily 9 am - 5 pm.
Preliminary meeting: In February, exact date will be announced soon.

Place: Campus Mitte, Vorchowweg 6 (or surrounding Charité Campus), 10117 Berlin

Research AG Volkamer

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

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.

Practical course date: 18.03.-22.03.2019, 5 days, daily 9 am - 5 pm.
Preliminary meeting: In February, exact date will be announced soon.

Place: Campus Mitte, Vorchowweg 6 (or surrounding Charité Campus), 10117 Berlin

Research AG Volkamer

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

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.

Practical course date: 18.03.-22.03.2019, 5 days, daily 9 am - 5 pm.
Preliminary meeting: In February, exact date will be announced soon.

Place: Campus Mitte, Vorchowweg 6 (or surrounding Charité Campus), 10117 Berlin

Research AG Volkamer

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.