The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets -- the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

翻译：COVID-19大流行凸显了开发更高效分子发现途径的紧迫性。对广阔化学空间的彻底探索是行不通的,因此发现新出现的药物目标蛋白质的新抑制分子是具有挑战性的,特别是对于结构或链条不明的目标而言。我们展示了单一深层基因框架的广泛效用,以发现新颖的药物类抑制分子,对付两个不同的SARS-CoV-2目标 -- -- 尖峰蛋白质的主要蛋白质和受体约束域(RBD)。为了进行目标认知设计,框架采用了一个从基因化模型对新分子进行目标序列定序取样的方法。观测到,每个目标的两个候选对象(四个合成的)的体内抑制剂。最强大的RBD抑制剂还作为一种稀有的非covalent抗病毒性抗病毒剂出现,与若干SARS-COV-2变体的活性病毒中和性病毒中和性变体进行广泛的活动。这些结果显示,可广泛部署的机器情报框架可以加速在不同新兴药物目标的发现。