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.

翻译：为了设计SARS-COV-1和SARS-COV-2的新抑制器,我们提议一般分子优化框架,即分子神经测定搜索(MONAS),由三部分组成:一个属性预测器,用以识别具有特定可取特性的分子;一个能源模型,该模型在统计上接近某一分子与已知训练分子的相似性;以及一个分子搜索方法。在这项工作中,这些部件分别与图形神经网络、深能模拟网络和蒙特卡洛树搜索(MCTS)同时,在统计上接近用于培训GNN的数据集。