Our research topics are part of the wide range of themes that exist at the ICSN. The rational design of active molecules using molecular modelling is an important research direction, making it possible to identify the structural determinants responsible for the activity and selectivity observed experimentally for a series of molecules. This can be used to optimise the structure of the ligands and thus improve their biological properties. A significant part of our activity is dedicated to projects linked to antibiotic resistance, in particular the study of structure-function relationships in the β-lactamase family (using modelling techniques as well as X-ray crystallography), the structural analysis of bacterial envelope proteins (in collaboration with Prof. Jean-François Collet from de Duve Institute in Brussels) and the prediction of antibiotic resistance from genomic data using machine learning and deep learning approaches (as part of the PPR Antibioresistance Seq2diag project). We are also working on methodological developments by perfecting efficient protocols for docking and virtual screening, free energy calculation and data encoding for machine learning and deep learning algorithms.
Our projects involve a wide range of techniques, mainly docking and virtual screening, the creation of targeted ligand libraries, model building by homology and molecular dynamics. Our virtual ligand libraries contain synthetic products, natural products and derivatives of natural products, with covalent and non-covalent interaction modes. The targets studied are soluble or membrane-bound, using both structure-based and ligand-based approaches. We use complementary approaches, adapted to the specific nature of each project. This work is carried out in collaboration with experimental teams at ICSN or elsewhere.