Small molecules with multi-target activity, also termed promiscuous compounds, are increasingly considered for pharmaceutical applications. The use of promiscuous chemical entities represents a departure from the compound specificity paradigm, one of the pillars of modern drug discovery. The popularity of promiscuous compounds is due to the concept of polypharmacology; another more recent drug discovery paradigm. It refers to insights that the efficacy of drugs often depends on interactions with multiple targets. Views concerning the extent to which small molecules might form well-defined interactions with multiple targets often differ, but comprehensive experimental investigations of promiscuity are currently rare. On the other hand, large volumes of active compounds and experimental measurements are becoming available and enable data-driven analyses of compound selectivity versus promiscuity. In this perspective, we discuss computational methods and data structures designed for promiscuity analysis. In addition, findings from large-scale exploration of activity profiles of inhibitors covering the human kinome are summarized. Although many kinase inhibitors are expected to be promiscuous, they are frequently found to be selective, which provides opportunities for target-directed drug discovery (rather than polypharmacology). We also discuss that machine learning yields evidence for the existence of structure-promiscuity relationships.

中文翻译：

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用于计算化合物混杂性分析的数据结构以及对人kinome抑制剂的示例性应用。

具有多靶标活性的小分子，也被称为混杂化合物，越来越多地被考虑用于药物应用中。混杂化学实体的使用代表了化合物特异性范式的背离，化合物范式是现代药物发现的支柱之一。混杂化合物的流行是由于多药理学的概念。另一个较新的药物发现范例。它是指一些见解，即药物的功效通常取决于与多个靶标的相互作用。关于小分子可能与多个靶标形成明确定义的相互作用的程度的观点常常不同，但是目前很少进行关于滥交的全面实验研究。另一方面，大量的活性化合物和实验测量值变得可用，并使数据驱动的化合物选择性与滥交性的分析成为可能。从这个角度出发，我们讨论了为混杂分析设计的计算方法和数据结构。另外，总结了大规模探索覆盖人类kinome的抑制剂活性谱的发现。尽管预计许多激酶抑制剂是混杂的，但经常发现它们具有选择性，这为靶标定向药物发现（而不是多药理学）提供了机会。我们还讨论了机器学习为存在结构-混杂关系提供了证据。我们讨论了为混杂分析设计的计算方法和数据结构。另外，总结了大规模探索覆盖人类kinome的抑制剂活性谱的发现。尽管预计许多激酶抑制剂是混杂的，但经常发现它们具有选择性，这为靶标定向药物发现（而不是多药理学）提供了机会。我们还讨论了机器学习为存在结构-混杂关系提供了证据。我们讨论了为混杂分析设计的计算方法和数据结构。另外，总结了大规模探索覆盖人类kinome的抑制剂活性谱的发现。尽管预计许多激酶抑制剂是混杂的，但经常发现它们具有选择性，这为靶标定向药物发现（而不是多药理学）提供了机会。我们还讨论了机器学习为存在结构-混杂关系提供了证据。这为靶向药物研发（而非多元药理学）提供了机会。我们还讨论了机器学习为存在结构-混杂关系提供了证据。这为靶向药物研发（而非多元药理学）提供了机会。我们还讨论了机器学习为存在结构-混杂关系提供了证据。