DeepMolGen

Designing new molecules is at the core of chemistry, but in practice, creating molecular structures with useful properties is a difficult and time-consuming task, simply because there are so many possibilities of connecting different atoms together to form new molecules, most of which are useless. In this project, we use a new class of machine learning techniques, called diffusion models, that is able to learn how to generate new interesting molecules. Diffusion models have already proven to be very useful in generating artistic images, movies, music, and speech, and recently also for generating pharmaceutical drugs. We will develop and apply diffusion models to (1) recover the atomistic details from low-resolution simulations of polymers, (2) optimize enzyme structures, and (3) design better catalyst materials for CO2 conversion into useful chemicals.