Efficient Large Scale Computing in Life Sciences

Models in the life sciences are becoming increasingly accurate in terms of their approximation to reality. In parallel, new technologies and techniques for computing such large and data-intensive problems on high-performance computers are also developing. In our group we bridge this gap by designing life science models that can be efficiently computed on high performance computers. This focus enables us to compute realistic models that can then be used in other life science disciplines.
One research field of our group is the development of  new drugs, which is typically time consuming and expensive  wet lab experiments are necessary. Structure-based virtual screening  has the potential to accelerate this by virtually fitting potential drug  candidates (compounds) into a receptor. This process requires a high  computational effort and asks for smart parallel strategies on computer  clusters.Using hierarchical strategies, AI-based approaches, and  mathematical methods, we aim to make virtual screening highly efficient  and close to application.
Specifically, we are working on extensions to the open source screening  platform Virtual Flow, which scales perfectly and provides a freely  available library of more than 1.4 billion molecules.