Project Tano

The current COVID-19 pandemic is putting a lot of pressure on the pharmaceutical industry to quickly come up with a solution. However, due to the nature of drug and vaccine development, speeding up the process remains problematic.\n Recognizing this challenge, the Joint European Disruptive Initiative (JEDI) has created a moonshot project in an attempt to accelerate the discovery of a cure through radical innovation. In particular, JEDI has created a challenge called «Billion Molecules against COVID-19». One of its objectives is to screen virtual libraries of molecules consisting of at least one billion entries in order to identify—within 33 days—potential molecules that could have a therapeutic effect against COVID-19.\n Our aim is to participate in this challenge by applying methods developed in the field of artificial intelligence. These methods have already shown great promise in the scope of de novo drug design. While the amount of data available on COVID-19 is limited, data for related problems is abundant within public databases. Our research goal is to leverage the intrinsic knowledge in those public databases in conjunction with the data available for COVID-19. We plan to do this through the use of transfer learning—a method that can be used to learn task relevant features, thus allowing for solutions within a low data regime. If successful, we may be able to find molecules capable of stopping the progression of COVID-19. These compounds will be made publicly available according to the JEDI challenge rules.