It is a well-known fact that there are often side effects to the long-term use of certain medications. These side effects can vary from mild dizziness to, at its most serious, death. The main factors that cause these side effects are the chemical composition, the mode of treatment, and the dose. The dynamics that govern the reaction of a drug heavily depend on its structural composition. The structural composition of a drug is defined by the structural arrangement of the corresponding basic chemical functional groups. Hence, it is essential to investigate the effect of chemical functional groups on the side effects to synthesize drugs with minimal side effects. To support this process, we developed a framework named MedFused (Medical Functional Group Side Effects Database), which is composed of drugs (International Union of Pure and Applied Chemistry: IUPAC nomenclature), functional groups, and the side effects along with other valuable information such as STITCH (search tool for interactions of chemicals) compound ID, and the Unified Medical Language System (UMLS) concept ID. We develop a web framework that functions on the MedFused system database on top of the Django web framework. Our web server supports functionalities such as exploring the database and descriptive graph tools, which provide additional exploration capabilities to the framework. These descriptive tools include histograms, pie charts, and association charts, which further explore the system. Above these basic tools, MedFused includes functionality to discover the drug's “chemical functional group” impact on “side effects”. The method conducts an association rule analysis on the relationships by considering the MedFused database as a collection of transactions. A specific transaction has a list of the functional groups of a drug and one side effect. Hence, a drug that has more than one side effect forms multiple transactions. Next, we generate a binary feature matrix based on the transactions and introduce a pruning mechanism to consider only the potential functional groups and side effects based on their support (frequencies), subjected to a predefined threshold (which can be changed accordingly). As the current version of the MedFused database has a limited number of side effects (hence low support), we restricted the analysis to identify the functional groups which have the most potential of causing a particular side effect, based on a confidence value of 1. Our framework can be further extended with more functions and tools as it supports the model view controller (MVC) architecture, which is inherited from the Django Python web framework.

一个众所周知的事实是长期使用某些药物经常会产生副作用。这些副作用可能从轻度的头晕到最严重的死亡不等。引起这些副作用的主要因素是化学成分，治疗方式和剂量。控制药物反应的动力学很大程度上取决于其结构组成。药物的结构组成由相应的基本化学官能团的结构排列决定。因此，有必要研究化学官能团对副作用的影响以合成具有最小副作用的药物。为了支持此过程，我们开发了一个名为MedFused（医疗功能组副作用数据库）的框架，它由药物（国际纯粹与应用化学联合会：IUPAC命名法），官能团，副作用以及其他有价值的信息（例如STITCH（化学物质相互作用的搜索工具）化合物ID和统一医学语言系统）组成（UMLS）概念ID。我们开发了一个在Django Web框架之上可在MedFused系统数据库上运行的Web框架。我们的Web服务器支持诸如探索数据库和描述性图形工具之类的功能，这些功能为框架提供了额外的探索功能。这些描述性工具包括直方图，饼图和关联图，它们将进一步探索该系统。在这些基本工具之上，MedFused包括发现药物的“化学官能团”对“副作用”的影响的功能。该方法通过将MedFused数据库视为事务的集合来对关系进行关联规则分析。特定交易包含药物功能组的列表和一个副作用。因此，具有一种以上副作用的药物会形成多个交易。接下来，我们基于事务生成二进制特征矩阵，并引入修剪机制，以基于潜在的功能组和副作用（基于它们的支持（频率））来考虑它们，并对其进行预定义的阈值（可以相应更改）。由于MedFused数据库的当前版本具有有限的副作用（因此支持率较低），因此我们基于置信度值为1限制分析以识别最有可能引起特定副作用的官能团。