I'm a Postdoc in the Computational Drug Design group of Prof. Gerard van Westen. I'm developing and applying multitask machine learning approaches with uncertainty quantification for affinity predictions for de novo generation of polypharmacological and selective drug compounds. Before joining the team in April 2021, I was a freshly graduated Dr in computational chemistry from the Paris-Saclay University with a research background in the statistical mechanics of liquids and the computational study of solvation phenomena. My academic career started as a student at the University Pierre et Marie Curie (now Sorbonne University) in Paris, France. During my five years there), I obtained a bachelor's degree in chemistry and physics followed by a master's degree in computational chemistry. During my first master's internship, I examined ion-specific condensation onto polyelectrolytes via molecular dynamics simulations and the Poisson–Boltzmann equation in the group of Dr Maria Sammalkorpi at the Aalto University. I did my master's thesis followed by a PhD under the supervision of Dr Daniel Borgis and Dr Maximilien Levesque at Maison de la Simulation, CEA and Department of Chemistry, École normale supérieure. I worked on the prediction of hydration free energies of drug-like molecules with methods that are as fast and precise as possible, with the aim that they could be used in drug design processes. I concentrated on two original approaches: out-of-equilibrium molecular simulations and a liquid state theory, the molecular density functional theory. I also used some machine learning approaches to improve hydration free energy predictions.

Publications