Computational methods for drug discovery such as virtual screening and more recently, machine learning models are slowly changing the way drug discovery is done. However, in pre-clinical drug discovery, the most challenging part of optimizing the desired properties of lead compounds after they have been identified is still mostly done by hand. My research focuses on the development of machine learning methods for the de-novo generation of new molecules in order to help chemists optimize lead compounds to drug candidates. More specifically, I am addressing the challenge of designing compounds with desired polypharmacology and selectivity patterns against the protein family of metallo-β-lactamases for the treatment of antibiotic resistant bacteria.