OpeNNdd (Open Neural Networks for Drug Discovery) is a software platform designed to empower students and citizen-scientists to interactively design drug therapeutics. This platform employs convolutional neural networks to quickly and accurately predict the the interaction energy of a ligand and an enzyme generated by a virus. If these predictions are accurate, users could use metrics returned by the neural network to design inhibitors that may be useful in creating medicine for treating viral diseases. Current efforts at Berkeley Lab have been focused on building an accurate machine learning model and optimized data pipeline for OpeNNdd. By making the process of designing medicine more accessible, OpeNNdd aims to help crowdsource medical solutions for newly emerging and re-emerging diseases around the world.