EUROPIN Summer School on Drug Design – Vienna

September 10 – 15, 2023

Welcome to #SSDD23

The summer school especially focuses on students and professionals who have a clear interest in learning the basics as well as latest developments in Pharmacoinformatics. The program provides an overview on main approaches and techniques used in computational drug design, ranging from protein modeling, docking, pharmacophore based screening, up to machine learning, data science, and workflows. During the afternoon sessions the participants will have the opportunity to obtain hands-on training via computational exercises.

Topics Coverage

  • Structure-based Design & Molecular Dynamics Simulations
  • Machine Learning & Deep Learning in Drug Design
  • Pharmacophore Modeling
  • in silico Toxicology
  • Open Data, Open Tools, Open Knowledge
  • Case Studies from Industry

150 participants + 45 speaker + 11 workshops! (click on photo to go to gallery)

We would like to thank you all for a week full of interesting and insightful talks and workshops as well as entertaining social events. We had a lot of fun! We look forward to seeing some of you again hopefully at the next #SSDD25.

Feedback #SSDD19

One of the best organized events I have attended. Congratulations, it was a great week of learning and socializing. Will most definitely return on following editions.
This summer school brought me a lot of knowledges, it's a start to get into my career. Thank you very much.
Great summer school! Thank you all so much for this awesome week in Vienna!

Contact us

If you have any questions regarding the EUROPIN Summer School on Drug Design, please don’t hesitate to contact us.

EUROPIN – a structured, highly interconnected training through research PhD program on the efficient and innovative use of information technologies and computational approaches in the drug discovery, design and development processes.

Organised by

Pharmacoinformatics Research Group 

Following a holistic pharmacoinformatic approach we combine structural modeling of proteins, structure-based drug design, chemometric and in silico chemogenomic methods, statistical modeling and machine learning approaches to develop predictive computational systems for transporters and ion channels.

The validation and optimisation of the obtained in silico models by strong links to experimental groups is an integral part of these activities.

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