A current limitation of computational approaches in academic settings is related to the relatively limited amount of computational processing capacity. However, over the next few years accessibility to cost effective, highly efficient hardware could increase dramatically, reducing budgetary and time requirements for developing and evaluating new ML algorithms. Other essential challenges discussed during the meeting included the application of chemoinformatics and AI methods to better understand unexplored, rare, and neglected diseases. More consistent communication and collaboration between academia, start-ups, and large industries is also desirable in order to foster a viable synergy and help the transfer of in silico knowledge ultimately to the clinic.