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DPDR-CPI a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome

机译:DPDR-CPI通过化学-蛋白质相互作用组预测药物定位和药物重新定位的服务器

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摘要

The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational “drug candidate positioning” or “drug positioning”, to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via .
机译:在过去的几年中,开发新药的成本急剧增加。为了确保合理的投资回报,对于行业和学术界的药物发现研究人员来说,找出所有可能的早期管道分子适应症是有用的。我们首次提出了计算上的“候选药物定位”或“药物定位”一词来描述上述过程。它与药物重新定位不同,后者可确定现有药物的新用途并最大程度地发挥其价值。由于许多治疗作用是由意想不到的药物-蛋白质相互作用介导的,因此分析化学-蛋白质相互作用组(CPI)分布图以预测适应症是合理的。在这里,我们介绍了服务器DPDR-CPI,该服务器只能基于小分子的结构进行实时预测。当用户提交一个分子时,服务器会将其停靠在611种人类蛋白质上,从而生成可用于预测的功能的CPI概况。可以列出输入分子与约1,000种人类疾病相关的可能性,并列出最重要的预测。在10倍交叉验证中,DPDR-CPI的总体AUROC为0.78,而在独立验证中的总体AUROC为0.76。可通过访问该服务器。

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