New machine learning method speeds up drug screening hundred-fold

The University of Eastern Finland performed a virtual search of 1.56 billion molecules to test two drug candidates. This was the world’s most extensive screening of its kind. Most drugs available today have been designed so that the target molecules are the body’s own proteins. Once the structure of one member of a protein family has been determined, the structure of other proteins in the same family can be predicted through modelling. A successful drug can be developed, for example, by screening a large library to find a molecule with a three-dimensional structure enabling interaction with the target protein. Professor Antti Poso’s research team were looking for molecules that would react with SurA chaperone and cyclin-G-associated kinase (GAK), two candidates with medicinal effect. The project tested the HASTEN algorithm developed for the screening, and created a new machine learning model.