We develop modern computational strategies to simulate chemistry and biology in complex environments. A particular effort is focused on the simulation of photochemistry happening in solvent and biological environments via QM/MM approaches and enhanced sampling techniques. We develop our own tools and contribute to open source simulation packages.
An applicative target of our research is the simulation of the mechanims of action of small organic ligands and fluorescent probes working as protein and DNA binders. We focus on characterization and design of new potential ligands and targets.
We develop machine learning techniques as a support of traditional computational methods. We employ artificial neural networks and other architectures to solve more efficiently the scientific problems we face.
A milestone in our research is the development of user friendly, open source tools that are designed and developed from the users' perspective. Our goal is to provide efficient and useful code and application that might speed up scientific discovery without the need of reprogramming from scratch.