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Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy

机译:过程药理学:药物开发和治疗的药理学数据科学方法

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摘要

A novel functional‐genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in “big data” providing comprehensive information about the drugs’ targets and their functional genomics is proposed. In “process pharmacology”, drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high‐dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks.
机译:提出了一种新颖的基于功能基因组学的药理学概念,该概念使用人工智能技术在“大数据”中进行挖掘和知识发现,从而提供有关药物靶标及其功能基因组学的全面信息。在“过程药理学”中,药物与生物学过程有关。这使被认为是一种或几种细胞过程中活性改变的疾病成为药物治疗的重点。在这种情况下,分子药物靶标仅仅是中间体。确定用于治疗或再利用的药物是基于药物影响的生物过程的高维空间中的相似性。将这一原理应用于与淋巴细胞性白血病相关的数据,可以确定候选药物的简短列表,其中包括最近被提议作为淋巴细胞性白血病的新型抢救药物的候选药物。药理数据科学方法可为开发和重新定位任务中的候选药物提供成功的选择。

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