Similarity search in large compound databases
In our research we develop, evaluate and use computational methods and protocols for drug design and discovery. One of our current main research projects is to investigate the use of machine learning methods to increase the throughput of virtual screening of ultra-large compound libraries with the aim to identify novel starting points for antimicrobial drugs, PET tracers and enzyme inhibitors. We have the ongoing NAISS project 203/5-337 that covers the bulk of computational resources to our projects but we now need access to Tetralith for a specific resource provided by PI Jens Carlsson at that system. At Tetralith, Jens' group has prepared an ultra-large compound library, not available elsewhere, and scripts for similarity search and compound extraction that we will apply for initial virtual screening hits in our projects.