论文标题
发现与生物瓶颈模型的共vid的协同药物组合
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models
论文作者
论文摘要
药物组合在治疗药物中起着重要作用，因为它的疗效更好和毒性降低。最近的方法应用了机器学习以识别癌症的协同组合，但它们不适用于具有有限组合数据的新疾病。鉴于药物协同作用与生物学靶标紧密相关，我们提出了一种共同学习药物 - 靶标相互作用和协同作用的\ emph {生物瓶颈}模型。该模型由两个部分组成：靶标相互作用和目标 - 疾病关联模块。该设计使该模型能够\ emph {解释}生物靶标如何影响药物协同作用。通过利用其他生物学信息，我们的模型仅使用90种共同药物组合进行训练，在药物协同预测中实现0.78测试AUC。我们在美国国家前进的转化科学（NCAT）设施中测试了模型预测，并发现了两种新型药物组合（Remdesivir + Reserpine和Remdesivir + IQ-1S），并具有强大的体外协同作用。
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Recent approaches have applied machine learning to identify synergistic combinations for cancer, but they are not applicable to new diseases with limited combination data. Given that drug synergy is closely tied to biological targets, we propose a \emph{biological bottleneck} model that jointly learns drug-target interaction and synergy. The model consists of two parts: a drug-target interaction and target-disease association module. This design enables the model to \emph{explain} how a biological target affects drug synergy. By utilizing additional biological information, our model achieves 0.78 test AUC in drug synergy prediction using only 90 COVID drug combinations for training. We experimentally tested the model predictions in the U.S. National Center for Advancing Translational Sciences (NCATS) facilities and discovered two novel drug combinations (Remdesivir + Reserpine and Remdesivir + IQ-1S) with strong synergy in vitro.
