当前位置: X-MOL 学术ChemRxiv › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Comprehensive Integrated Drug Similarity Resource for In-Silico Drug Repositioning and Beyond
ChemRxiv Pub Date : 2020-05-29 , DOI: 10.26434/chemrxiv.12376505.v1
AKM Azad 1 , Mojdeh Dinarvand , Alireza Nematollahi , Joshua Swift , Louise Lutze-Mann , Fatemeh Vafaee
Affiliation  

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.




中文翻译:

全面的整合式药物相似性资源,可在硅药物内部重新定位和超越

药物相似性研究受到以下假设的推动:相似药物应显示相似的治疗作用,因此可以潜在地治疗相似的疾病。药物-药物的相似性是通过各种直接和间接的证据来源得出的,并且在发现经过验证的重新定位候选药物以及其他硅药物开发应用中经常显示出很高的预测能力。但是,现有资源要么覆盖范围有限,要么依赖单独的证据来源,从而忽略了与毒品有关的数据来源的丰富性和多样性。因此,迫切需要一种综合资源,该资源整合各种与毒品有关的信息以得出多证据的毒品-药物相似性。我们通过为一整套详尽的小分子药物(当前版本总计10,367种)收集异类信息,并系统地整合了多种证据来源,以得出多模式药物-药物相似性网络,从而解决了这一资源缺口。由此产生的数据库DrugSimDB当前包含238,635个药物对,这些药物对具有明显的相似性,并辅以交互式的用户友好型Web界面(http://vafaeelab.com/drugSimDB.html),不仅使数据库易于访问,搜索,过滤和出口,还提供了有关所查询药物及其相互作用的各种补充信息。整合方法可以将更多药物信息灵活地整合到相似性网络中,从而提供易于扩展的平台。


更新日期:2020-05-29
down
wechat
bug