Dr. Klambauer talked about the drug discovery efforts at the ELLIS group he leads in Linz. The drug discovery approach is to find small molecules that can inhibit some of the machineries of the virus that are needed for its replication and function. COVID-19 virus has a handful of proteins that can be targeted by a molecule found using a drug discovery approach. Machine learning can contribute to the drug discovery process in various stages such as compound design and virtual screening. In compound design, methods like variational autoencoder, generative adversarial networks, reinforcement learning, and recurrent neural networks have been used to build a molecule from representations of atoms in a proper space. Virtual screening refers to using computers to search through a database of available molecules to find the promising ones for inhibiting the virus function. Drug discovery can normally take many years to find an FDA approved drug especially due to the time needed for screening through compounds and preclinical trials. Machine learning can facilitate these steps and find those molecules which have a better chance to pass clinical trials with less side effects. There is also a drug discovery challenge organized by JEDI called “a billion molecules against COVID-19” to find a list of the lead compounds by screening a billion molecules and narrowing the search down to find an immediately usable molecule which is already FDA approved.