bioinformatics and computational biology at UNSW Sydney.
Drug discovery and microbiology at UNSW Sydney.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa126.
Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information.
药物相似性研究是基于这样一种假设，即相似的药物应该具有相似的治疗作用，因此可能能够治疗类似的疾病。药物-药物相似性已经通过各种直接和间接的证据来源得出，并在发现经过验证的再定位候选药物以及其他基于计算机的药物开发应用方面经常显示出很高的预测能力。然而，现有的资源要么覆盖范围有限，要么依赖于单一的证据来源，忽略了丰富多样的药物相关数据源。因此，人们一直需要一个综合性的资源，该资源整合了各种药物相关信息，以得出多证据的药物-药物相似性。我们通过为一整套小分子药物（当前版本共 10367 种）编译异构信息来解决这一资源差距，并系统地整合多种证据来源，以得出一个多模态的药物-药物相似性网络。由此产生的数据库“DrugSimDB”目前包含 238635 对具有显著聚集相似性的药物对，同时还提供了一个交互式用户友好的网络界面（http://vafaeelab.com/drugSimDB.html），不仅可以方便地访问、搜索、筛选和导出数据库，还提供了关于查询药物和相互作用的各种补充信息。该集成方法可以灵活地将更多的药物信息纳入相似性网络，提供一个易于扩展的平台。数据库编译和构建的源代码已经有详细的记录，并实现了半自动化，以便随时升级，以涵盖新药和最新的药物信息。
