Xue L, Bajorath J
Computer-Aided Drug Discovery, New Chemical Entities, 18804 North Creek Pkwy, Bothell, Washington 98011, USA.
Comb Chem High Throughput Screen. 2000 Oct;3(5):363-72. doi: 10.2174/1386207003331454.
Many contemporary applications in computer-aided drug discovery and chemoinformatics depend on representations of molecules by descriptors that capture their structural characteristics and properties. Such applications include, among others, diversity analysis, library design, and virtual screening. Hundreds of molecular descriptors have been reported in the literature, ranging from simple bulk properties to elaborate three-dimensional formulations and complex molecular fingerprints, which sometimes consist of thousands of bit positions. Knowledge-based selection of descriptors that are suitable for specific applications is an important task in chemoinformatics research. If descriptors are to be selected on rational grounds, rather than guesses or chemical intuition, detailed evaluation of their performance is required. A number of studies have been reported that investigate the performance of molecular descriptors in specific applications and/or introduce novel types of descriptors. Progress made in this area is reviewed here in the context of other computational developments in combinatorial chemistry and compound screening.
计算机辅助药物发现和化学信息学中的许多当代应用都依赖于通过描述符来表示分子,这些描述符能够捕捉分子的结构特征和性质。此类应用包括多样性分析、库设计和虚拟筛选等。文献中已报道了数百种分子描述符,从简单的整体性质到精细的三维公式和复杂的分子指纹,后者有时由数千个比特位组成。基于知识选择适用于特定应用的描述符是化学信息学研究中的一项重要任务。如果要基于合理依据而非猜测或化学直觉来选择描述符,就需要对其性能进行详细评估。已有多项研究报道了对分子描述符在特定应用中的性能进行研究和/或引入新型描述符的情况。在此结合组合化学和化合物筛选中的其他计算进展,对该领域取得的进展进行综述。