My work lies at the interface of chemistry, biophysics, and computer science. Here are some research themes of current interest.

Computer-Aided Molecular Design

Most broadly, I am interested in how computation can be leveraged in the design of organic and biological molecules for biomedical and nanotechnological applications. Many of the properties that make these molecules attractive as therapeutics, catalysts, or sensors depend on the complex interplay between intra- and inter-molecular interactions, and solvent, electronic, and entropic effects. Using data-driven and physics-based approaches, I aim to predict these molecular properties, as well as to understand their atomic-level determinants.

Chemical Science 2020, 11, 1140-1152
ACS Central Science 2018, 4(12), 1708-1718
Journal of the American Chemical Society 2017, 139(2), 946-957
Chemical Science 2016, 7, 207-218

Machine Learning and Experimental Design

I am interested in the application of machine learning algorithms to property prediction, chemical space exploration, and experiment planning. These include discriminative models like random forest, Gaussian processes, and neural networks, for predicting molecular properties; Bayesian optimization algorithms to guide efficient, possibly autonomous, experimental and computational studies; and generative models like variational autoencoders and generative adversarial networks for inverse molecular design.

ACS Central Science 2019, 5(8), 1468-1474

Molecular Dynamics and Biophysics

I use Molecular Dynamics (MD) and Monte Carlo (MC) simulations to study at the atomic level the multitude of conformations available to molecules in solution, and to extract information about their thermodynamic, kinetic, and dynamical properties. Being rooted in physical principles, these simulations allow for the atomic-level interpretation of the role of dynamics, inter-molecular interactions, and solvation in biological function and dysfunction.

Nature Communications 2019, 10, 925
Communications Chemistry 2018, 1, 19