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首页> 美国政府科技报告 >Physicochemical Descriptor-Based Scoring Scheme for Effective and Rapid Filtering of Kinase-Like Chemical Space.
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Physicochemical Descriptor-Based Scoring Scheme for Effective and Rapid Filtering of Kinase-Like Chemical Space.

机译：基于物理化学描述符的有效快速过滤激酶样化学空间的评分方案。

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Background: The current chemical space of known small molecules is estimated to exceed 10(exp 60) structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors. Results: The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinaselikeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinaselikeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree. Conclusions: Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening.

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