Machine learning and network medicine approaches for drug repositioning for COVID-19.
Machine learning and network medicine approaches for drug repositioning for COVID-19.
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DOI：
10.1016/j.patter.2021.100396
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发表时间：
2022-01-14
期刊：
影响因子：
--
通讯作者：
Paccanaro A
中科院分区：
文献类型：
--
作者：
Santos SS;Torres M;Galeano D;Sánchez MDM;Cernuzzi L;Paccanaro A
我们提出了两种用于药物再利用的机器学习方法。虽然我们已经为COVID-19开发了它们，但它们是疾病不可知的。这两种方法是互补的，分别针对SARS-CoV-2和宿主因素。我们的第一种方法包括一个矩阵分解算法排名广谱抗病毒药。我们的第二种方法，基于网络医学，使用图形内核来排名药物，根据它们在人类相互作用组的子网络上引起的扰动，这对SARS-CoV-2感染/复制至关重要。我们的实验表明，我们预测的最佳广谱抗病毒药物包括适用于COVID-19患者的同情使用的药物;并且我们基于内核的方法获得的排名与实验数据一致。最后，我们介绍了COVID-19重新定位探索器（CoREx），这是一个交互式在线工具，用于在生物网络，蛋白质功能，药物临床使用和连接图的背景下探索药物和SARS-CoV-2宿主蛋白之间的相互作用。CoREx可在https://paccanarolab.org/corex/上免费获得。用于广谱抗病毒药物再利用的矩阵分解模型用于对药物在相互作用组上诱导的扰动进行建模的图核方法图核可以整合转录组学数据以改善药物再利用CoREx：一个免费的在线工具，用于制定针对COVID-19的药物再利用假设通过重新使用市场上已有的药物，可以显著缩短针对紧急病毒性疾病的治疗的开发时间轴-这个概念被称为药物重新定位。我们提出了两种互补的机器学习方法，分别针对SARS-CoV-2和宿主因素进行药物重新定位。我们的矩阵分解方法利用药物开发信息来预测广谱抗病毒药物的有效性。我们基于图核的方法，植根于网络医学的思想，预测哪些FDA批准的药物更有可能扰乱对SARS-CoV-2感染/复制至关重要的人类子网。我们还介绍了CoREx，这是一个免费的在线工具，使科学家能够在生物网络和药理学信息的背景下推理和制定关于药物再利用的假设。虽然我们已经为COVID-19开发了这些方法，但我们的方法可以应用于任何病毒性疾病。我们提出了两种互补的机器学习方法，用于针对COVID-19的药物重新定位，分别针对SARS-CoV-2及其在宿主中的细胞过程。我们的矩阵分解方法利用药物开发信息来预测广谱抗病毒药物;我们基于图形内核的方法，植根于网络医学的思想，预测哪些FDA批准的药物更有可能扰乱对SARS-CoV-2感染/复制至关重要的人类子网。我们还介绍了CoREx，一个免费提供的在线工具，推理和制定关于药物再利用的生物网络和药理学信息的背景下的假设。
We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/. A matrix decomposition model for repurposing broad-spectrum antivirals A graph kernel approach to model perturbations induced by drugs on the interactome Graph kernels can integrate transcriptomics data to improve drug repurposing CoREx: a free online tool to formulate hypothesis for drug repurposing for COVID-19 The development timeline for treatments against emergent viral diseases can be significantly reduced by re-using drugs already available on the market—a concept known as drug repositioning. We present two complementary machine learning approaches for drug repositioning that target SARS- CoV-2 and host factors, respectively. Our matrix decomposition approach exploits drug developmental information to predict the effectiveness of broad-spectrum antiviral drugs. Our graph kernel-based approach, rooted in ideas from network medicine, predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. We also introduce CoREx, a freely available online tool that enables scientists to reason and formulate hypotheses about drug repurposing in the context of biological networks and pharmacological information. While we have developed these methodologies for COVID-19, our approaches can be applied to any viral disease. We present two complementary machine learning approaches for drug repositioning against COVID-19 that target SARS-CoV-2 and its cellular processes in the host, respectively. Our matrix decomposition approach exploits drug developmental information to predict broad-spectrum antivirals; our graph kernel-based approach, rooted in ideas from network medicine, predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. We also introduce CoREx, a freely available online tool to reason and formulate hypothesis about drug repurposing in the context of biological networks and pharmacological information.
影响因子：
6
作者：
Sendama W
通讯作者：
Sendama W