2005/06/10 by Suxin Zheng, Xiaomin Luo, Gang Chen +4 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Analytical Chemistry and Chromatography #Chemical Synthesis and Analysis #Computational Drug Discovery Methods
paper · doi:10.1021/ci050031j
openalex publication_date 2005/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
To develop a new chemistry space filter with high efficiency and accuracy, an analysis on distributions of as many as 50 structural and physicochemical properties was carried out on both druglike and nondruglike databases, viz. MACCS-II Drug Data Report (MDDR), Comprehensive Medicinal Chemistry (CMC), and Available Chemicals Directory (ACD). Based on the analysis results, a chemistry space filter was developed that can effectively discriminate a druglike database from a nondruglike database. The filter is composed of two descriptors: one is a molecular saturation related descriptor, and the other is associated with the proportion of heteroatoms in a molecule. Both are molecular size independent. Therefore, the profiles of a druglike database could be characterized as proper molecular saturation and proper percentage of heteroatoms, revealing direct indices for designing and optimizing combinatorial libraries. The application of the new filter on the Chinese Natural Product Database (CNPD) suggested that CNPD is, as expected, a potential druglike database, testifying that the new filter is reliable. Therefore, this newly developed chemistry space filter should be a potent tool for identifying druglike molecules, thus, it would have potential applications in the research of combinatorial library design and virtual high throughput screening using computational approaches for drug discovery.