Figure A.1 shows the amino acid sequences corresponding to the three SARS-CoV -2 targets. We used a bidirectional Gated Recurrent Unit (GRU) with a linear output layer as an encoder. Figure B.1: The novelty of the scaffold of each generated molecule compared to the most similar scaffold in the training set. Similarity of the fingerprints, is shown next to the scaffold of each generated molecule. We show a representative set of molecules generated for each target in Figure D.1 Figure D.1: Representative molecules generated for (top to bottom): NSP9 Replicase, Receptor-Binding Domain (RBD) of S protein, and Main Protease of SARS-CoV -2 RBD has maximum subgraph similarity to a commercially available drug Telavancin (See Figure E.3).

BEGIN ARTICLE PREVIEW: By Jessica Kent October 29, 2020 – A deep learning tool can offer more information about SARS-CoV-2 proteins to accelerate COVID-19 drug discovery, according to a study published in Chemical Science. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media. Researchers from Michigan State University (MSU) Foundation repurposed deep learning models to focus on a specific SARS-CoV-2 protein called its main protease. The main protease is a cog in the virus’s protein machinery that’s critical to how the pathogen makes copies of itself. Drugs that disable the main protease could stop the virus from replicating. Dig Deeper The main protease is distinct from all known human proteases, which isn’t always the case. Drugs that attack the viral protease are therefore less likely to disrupt people’s natural biochemistry. The SARS-CoV-2 main protease is also almost identic