SmartGraph API: Programmatic Knowledge Mining in Network-Pharmacology Setting.

IF 5.3 2区化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-04 DOI:10.1021/acs.jcim.4c00789

Gergely Zahoránszky-Kőhalmi, Brandon Walker, Nathan Miller, Brett Yang, Dhatri V L Penna, Jessica Maine, Timothy Sheils, Ke Wang, Jennifer King, Hythem Sidky, Sridhar Vuyyuru, Jeyaraman Soundarajan, Samuel G Michael, Alexander G Godfrey, Tudor I Oprea

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Abstract

The recent SmartGraph platform facilitates the execution of complex drug-discovery workflows with ease in the network-pharmacology paradigm. However, at the time of its publication we identified the need for the development of an Application Programming Interface (API) that could promote biomedical data integration and hypothesis generation in an automated manner. This need was magnified at the time of the COVID-19 pandemic. This study addresses the absence of such an API. Accordingly, most functionalities of the original platform were implemented within the SmartGraph API. We demonstrate that by using the API it is possible to transform the original semiautomated workflow behind the Neo4COVID19 database to a fully automated one. The availability of the SmartGraph API lends a significant improvement to the programmatic integration of network-pharmacology-oriented knowledge graphs and analytics, as well as predictive functionalities and workflows.

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SmartGraph API：网络药理学设置中的程序化知识挖掘。

最近的SmartGraph平台在网络药理学范式中简化了复杂药物发现工作流程的执行。然而，在其出版时，我们确定了开发应用程序编程接口（API）的需求，该接口可以以自动化的方式促进生物医学数据集成和假设生成。在2019冠状病毒病大流行期间，这种需求被放大了。本研究解决了缺乏此类API的问题。因此，原始平台的大部分功能都在SmartGraph API中实现。我们证明，通过使用API，可以将Neo4COVID19数据库背后的原始半自动工作流转换为全自动工作流。SmartGraph API的可用性大大改善了面向网络药理学的知识图谱和分析的程序化集成，以及预测功能和工作流程。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.