Artificial Intelligence has gained remarkable traction in science and technology. Drug discovery has particularly benefited from the advent of deep learning, achieving success in molecule prioritization and automated de novo design. Here, deep learning can accelerate the navigation of the extremely vast chemical space of drug-like molecules in search for potential therapeutics and complement resource- and time-intensive high-throughput screening campaigns. Despite early successes, the full potential of AI in chemistry is still not fully untapped. The Molecular Machine Learning team at TU/e (led by Dr. Francesca Grisoni) aims to stretch the boundaries of AI for drug discovery, by melding chemistry, biology, and informatics, at the interface between computation and wet-lab experiments.