Deng Zhe, Tu Weizhong, Deng Zixin, Hu Qian-Nan
Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences , Wuhan, 430071, China.
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , 300308, Tianjin, China.
J Chem Inf Model. 2017 Oct 23;57(10):2395-2400. doi: 10.1021/acs.jcim.7b00175. Epub 2017 Oct 12.
The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of PhID entities or some drug-target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases.
当前的网络药理学研究遇到了瓶颈，大量公共数据分散在不同的数据库中。缺乏一个开放获取且整合的平台来整合这些信息以进行系统研究。为了解决这个问题，我们开发了PhID，一个整合药理学数据库，它整合了超过40万个药理学元素（药物、靶点、疾病、基因、副作用和通路）以及公共数据库分支中的超过20万个元素相互作用。PhID有三个主要应用：（1）帮助科学家通过名称、公共ID、分子结构或分子亚结构在海量的药理学元素相互作用数据中进行搜索；（2）通过基于网络的网络图帮助可视化药理学元素及其相互作用；（3）通过两个模块PreDPI-ki和FIM提供药物-靶点相互作用的预测，用户可以通过这两个模块预测PhID实体的药物-靶点相互作用或他们感兴趣的一些药物-靶点对。为了从系统层面理解药物作用和疾病复杂性，PhID作为一个网络药理学工具，从数据层、可视化层和预测模型层的角度建立起来，以呈现当前数据库未挖掘的信息。
