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With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, wepropose the general molecule optimization framework, Molecular Neural AssaySearch (MONAS), consisting of three components: a property predictor whichidentifies molecules with specific desirable properties, an energy model whichapproximates the statistical similarity of a given molecule to known trainingmolecules, and a molecule search method. In this work, these components areinstantiated with graph neural networks (GNNs), Deep Energy Estimator Networks(DEEN) and Monte Carlo tree search (MCTS), respectively. This implementation isused to identify 120K molecules (out of 40-million explored) which the GNNdetermined to be likely SARS-CoV-1 inhibitors, and, at the same time, arestatistically close to the dataset used to train the GNN.

Automatic design of novel potential 3CL$^{\text{pro}}$ and PL$^{\text{pro}}$ inhibitors