COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19.
COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19.
复制标题
DOI：
10.1038/s41598-021-98812-0
复制
发表时间：
2021-09-30
影响因子：
4.6
通讯作者：
Recanatini M
中科院分区：
文献类型：
--
作者：
Menestrina L;Cabrelle C;Recanatini M
COVID-19大流行造成了巨大的公共卫生问题，需要实施所有可用的手段来应对，药物就是其中之一。在这种情况下，我们观察到一个未得到满足的需求，即通过一个易于使用、免费访问的在线工具来描述正在进行的药物临床试验的不断发展的场景。从这一考虑出发，我们开发了COVIDrugNet（http：//compmedchem.unibo.it/covidrugnet），这是一个网络应用程序，允许用户捕获整体视图，并随时了解临床药物研究如何应对SARS-CoV-2感染。在这里，我们描述了Web应用程序，并通过一些例子展示了如何在临床试验中探索治疗COVID-19的药物的整体情况，并尝试探索当前方法与现有生物学和药理学证据的一致性。我们的结论是，基于COVIDrugNet对COVID-19药物靶向系统的仔细分析可以帮助理解拟议药物选择的生物学意义，并最终改善对更有效疗法的研究。
The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet (http://compmedchem.unibo.it/covidrugnet), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.
登录
查看更多内容
影响因子：
14.9
作者：
Avram S;Bologa CG;Holmes J;Bocci G;Wilson TB;Nguyen DT;Curpan R;Halip L;Bora A;Yang JJ;Knockel J;Sirimulla S;Ursu O;Oprea TI
通讯作者：
Oprea TI
DOI：
10.1093/bioinformatics/btaa718
发表时间：
2021-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Korn D;Bobrowski T;Li M;Kebede Y;Wang P;Owen P;Vaidya G;Muratov E;Chirkova R;Bizon C;Tropsha A
通讯作者：
Tropsha A
DOI：
10.1073/pnas.2025581118
发表时间：
2021-05-11
影响因子：
11.1
作者：
Morselli Gysi D;do Valle Í;Zitnik M;Ameli A;Gan X;Varol O;Ghiassian SD;Patten JJ;Davey RA;Loscalzo J;Barabási AL
通讯作者：
Barabási AL
影响因子：
5.2
作者：
Beovic, Bojana;Dousak, May;Erdem, Hakan
通讯作者：
Erdem, Hakan
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R