Mapping the gene regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary aortic valve cells and was sufficient to prevent and treat aortic valve disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.

中文翻译：

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iPSC 衍生细胞中基于网络的筛选揭示了心脏瓣膜疾病的治疗候选者

绘制人类疾病中失调的基因调控网络将允许设计治疗核心疾病机制的网络校正疗法。然而，传统上筛选小分子最多对一到几个输出的影响，这会影响发现并限制真正的疾病修饰候选药物的可能性。在这里，我们开发了一种机器学习方法来识别小分子，这些小分子可以广泛纠正人类诱导多能干细胞 (iPSC) 疾病模型中失调的基因网络，该模型是一种常见的涉及主动脉瓣的心脏病。最有效的候选治疗药物 XCT790 的基因网络校正推广到患者来源的原代主动脉瓣细胞，足以在小鼠模型中预防和治疗体内主动脉瓣疾病。这个策略，