SCS Spring School on Digital Chemistry

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 will introduce, in hands-on sessions, the essential ingredients for a quicker adoption of data-driven solutions and more effective collaboration. The hands-on sessions will focus both on the application of existing open-source toolkits and the acquisition of skills for the development of new technologies. The emphasis will be on applications for drug and crop protection discovery.

Note: You will be working with your own laptop; a basic understanding of Python, i.e. being able to write a simple program, is 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