In recent years, we have seen an active shift towards data-driven decision making in chemistry with the goal of accelerating drug, crop protection, and materials discovery.

The approach's power is demonstrated by data-driven solutions that led to breakthroughs in synthesis prediction, protein structure prediction, and generative chemistry. The rapid progress in this area of research is being driven by a growing open-source community and transdisciplinary collaboration in the fields of cheminformatics, machine learning, and molecular modeling.

In this Summer School, in addition to reviewing the fundamental concepts of machine learning, cheminformatics, and molecular modeling, we introduced, in hands-on sessions, the essential ingredients for a quicker adoption of data-driven solutions and more effective collaboration. The hands-on sessions focused both on the application of existing open-source toolkits and the acquisition of skills for the development of new technologies. The emphasis was on applications for drug and crop protection discovery.

a basic understanding of Python, i.e. being able to write a simple program, was required.

There are quite a few sites that get you started:

• https://www.w3schools.com/python/
• https://docs.python.org/3/tutorial/index.html

Session Topics:

• Technology Basics & Best Practices in Python
• Chemical Data Science 
• Machine Learning Basics 
• Pipelines for ligand- and structure-based design
• Beyond software: CADD behind the scenes
• Reaction/Language models 

SCS-Syngenta-Idorsia Award for Best Presentation
Syngenta and Idorisa sponsored the award for the best poster presentations in the two categories "junior poster presenter" and "senior poster presenter". 

Winners Best Poster Presentation 2023:
Junior poster winner:
Ariel Francis Perez Mellor, University of Geneva
«Using Graph Theory to Unravel Born-Oppenheimer Molecular Dynamics and Replica-Exchange Monte Carlo Simulation»

Senior poster winners:
Bálint Tamás, University of Zurich
«Development of a self-optimizing platform for flow-based peptide synthesis»
and
Gerald Keller, University of Würzburg
«Interpreting and improving residence times of protein kinase A ligands by means of T-random accelerated molecular dynamics simulations»

Special contributors recognition:
Paula Linh Kramer