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本文报告一种新颖的基于骨架结构分类法的递进式药物筛选(PS-SCA)技术。此技术在美国国立癌症研究所(NCI)的八组细胞水平上的高通量筛选实验数据上进行了测试,结果表明,基于拓扑结构数据的递进式筛选可以极大地降低药物筛选的代价,缩短筛选时间。模拟实验证明,PS-SCA递进式筛选技术包括三个阶段:(1)骨架多样性采样筛选试验:(2)活性化合物发现试验;(3)可忽略的多余筛选试验。运用PS-SCA递进式筛选技术,可以在只筛选20％的化合物情况下找到最有意义的70-80％的活性化合物。而且,这70％-80％的活性化合物中包含了关键的结构骨架。
This article reports a novel progressive drug screening (PS-SCA) technique based on skeletal classification. This technique was tested on high-throughput screening data on eight cell lines of the National Cancer Institute (NCI). The results show that progressive screening based on topological data can greatly reduce the cost of drug screening, Shorten the screening time. Simulation experiments show that the PS-SCA progressive screening technology includes three stages: (1) skeleton diversity sampling and screening test: (2) active compound discovery test; (3) negligible redundant screening test. Using the PS-SCA Progressive Screening Technology, the most significant 70-80% of the active compound can be found by screening only 20% of the compounds. Moreover, 70% to 80% of the active compounds contain the key structural skeleton.