药物发现
范围(计算机科学)
药品
疾病
数据科学
计算生物学
计算机科学
药物重新定位
多样性(控制论)
生物
生物信息学
医学
药理学
人工智能
病理
程序设计语言
作者
Sarath Chandra Janga,Andreas G. Tzakos
摘要
Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.
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