Mechanistic and Machine Learning-driven Modelling in Bioengineering
At M3B, we strive to create informative computational models able to shed light on crucial biological processes.
At the smallest scale,
where the behaviour of single molecules is of interest, we rely on our expertise in computational biophysics to create molecular models of proteins, membranes and molecular assemblies for drug delivery. Our goal is to unveil key physical-chemical characteristics for a better understanding of drug- and ligand recognition (see VIRTUOUS project), and of the effects of mutations on protein conformation (see CRYSTAL project), and to drive the development of new anti-cancer compounds and delivery systems.
At a higher scale,
we employ Machine Learning not only to rapidly screen among chemical compounds for a desired endpoint effect, but also to unveil complex relationships between clinical and real-world variables and the (risk of) development of pathologies (also see PARENT project).
Thanks to the multidisciplinarity of our research group,
we are able to fruitfully intersect our competencies in molecular modelling and Machine Learning to create new multi-scale approaches to complex biological problems, ranging from pathology to drug discovery.