The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. Unfortunately, standard computational screening methods face difficulties in ranking diverse ligands to a receptor due to disparate ligand scaffolds and varying charge states. Here, we describe full density functional quantum mechanical (DFT) simulations of Mpro in complex with various ligands to obtain absolute ligand binding energies. Our calculations are enabled by a new cloud-native parallel DFT implementation running on computational resources from Amazon Web Services (AWS). The results we obtain are promising: the approach is quite capable of scoring a very diverse set of existing drug compounds for their affinities to M pro and suggest the DFT approach is potentially more broadly applicable to repurpose screening against this target. In addition, each DFT simulation required only ~ 1 h (wall clock time) per ligand. The fast turnaround time raises the practical possibility of a broad application of large-scale quantum mechanics in the drug discovery pipeline at stages where ligand diversity is essential.

COVID-19 大流行引发了前所未有的努力，以寻找可以降低相关发病率/死亡率的药物。计算化学方法有可能比实验方法更快地对潜在候选者进行分类。这些方法已广泛用于寻找能够抑制 SARS-CoV-2 复制周期中关键蛋白质的小分子。一个重要的目标是 SARS-CoV-2 主要蛋白酶 Mpro，这种酶可将病毒多蛋白切割成病毒复制和转录所需的单个蛋白质。不幸的是，由于不同的配体支架和不同的电荷状态，标准计算筛选方法在对受体的不同配体进行排序时面临困难。在这里，我们描述了 Mpro 与各种配体复合物的全密度功能量子力学 (DFT) 模拟，以获得绝对配体结合能。我们的计算是通过在 Amazon Web Services (AWS) 的计算资源上运行的新的云原生并行 DFT 实现来实现的。我们获得的结果是有希望的：该方法非常能够对一组非常多样化的现有药物化合物与 M pro 的亲和力进行评分，并表明 DFT 方法可能更广泛地适用于针对该靶点的重新筛选。此外，每个配体的每个 DFT 模拟仅需要约 1 小时（挂钟时间）。快速的周转时间提高了大规模量子力学在配体多样性至关重要的药物发现流程中广泛应用的实际可能性。