Selzer Paul, Roth Hans-Jörg, Ertl Peter, Schuffenhauer Ansgar
Information and Knowledge Management at Novartis, IK@N, InSilico Sciences, Novartis Institutes for BioMedical Research, CH-4002 Basel, Switzerland.
Curr Opin Chem Biol. 2005 Jun;9(3):310-6. doi: 10.1016/j.cbpa.2005.04.001.
The concept of complexity in chemistry has received much interest in the research community. Various measures to assess molecular complexity have been published, ranging from abstract complexity definitions to very specific application-oriented definitions. In this article we focus on molecular complexity in relation to biological activity. Connectivity and feature-based structural descriptors have been evaluated with reference to their potential as complexity measures. Our goal was to discuss the potential of the complexity concept to support the drug discovery process, helping to design suitable lead candidates. The studies have shown that highly active compounds, on average, are more complex than inactive compounds. However, complexity must be balanced with other molecular properties because more complex molecules have a higher probability to exhibit pharmacokinetic problems.
化学中的复杂性概念在研究界引起了广泛关注。已经发表了各种评估分子复杂性的方法，从抽象的复杂性定义到非常具体的面向应用的定义。在本文中，我们关注与生物活性相关的分子复杂性。基于连接性和特征的结构描述符已根据其作为复杂性度量的潜力进行了评估。我们的目标是讨论复杂性概念在支持药物发现过程方面的潜力，帮助设计合适的先导候选物。研究表明，平均而言，高活性化合物比无活性化合物更复杂。然而，复杂性必须与其他分子性质相平衡，因为更复杂的分子更有可能出现药代动力学问题。
