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