One particular focus of our group is the development of machine learning tools for predicting functional consequences of genetic variants that can be associated with a particular disease or resistance phenotype. In doing so, we aim to predict not only the direction and the magnitude of the effect, i.e. whether a certain variant is likely to be pathogenic or cause resistance to a drug, but also the exact molecular mechanism, which is responsible for it. We do so by combining phylogenetic methods with approaches from structural bioinformatics: computational modelling three-dimensional structure of proteins, their interactions, and dynamics, united in a robust machine learning framework.

A particular emphasis of this line of work is discovery of novel resistance mechanisms. Another focus of the research group is investigation of protein-drug interactions and drug-binding pockets with data-mining graph theory-based approaches. We aim to describe protein functional motifs and drug-binding patterns in them, and eventually develop novel machine-learning tool for prediction of drug affinity based on structural descriptors of protein-drug interactions.