The application of computational approaches in drug discovery has been consolidated in
the last decades. These families of techniques are usually grouped under the common …

Optimal control of diffusion processes is intimately connected to the problem of solving
certain Hamilton–Jacobi–Bellman equations. Building on recent machine learning inspired …

Dynamical reweighting methods permit to estimate kinetic observables of a stochastic
process governed by a target potential V ̃ (x) from trajectories that have been generated at a …

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of
complex molecular events. However, these simulations can rarely sample the required time …

Connecting optimal transport and variational inference, we present a principled and
systematic framework for sampling and generative modelling centred around divergences …

The analysis of extremes in climate models is hindered by the lack of statistics due to the
computational costs required to run simulations long enough to sample rare events. We …

Molecular dynamics (MD) simulations have been actively used in the study of protein
structure and function. However, extensive sampling in the protein conformational space …

We have shown recently that a Markov process conditioned on rare events involving time-
integrated random variables can be described in the long-time limit by an effective Markov …

Computational approaches currently assist medicinal chemistry through the entire drug
discovery pipeline. However, while several computational tools and strategies are available …

We introduce and test an algorithm that adaptively estimates large deviation functions
characterizing the fluctuations of additive functionals of Markov processes in the long-time …