Modeling promiscuity based on in vitro safety pharmacology profiling data
Modeling promiscuity based on in vitro safety pharmacology profiling data
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DOI：
10.1002/cmdc.200700036
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发表时间：
2007-06-01
期刊：
影响因子：
3.4
通讯作者：
Urban, Laszlo
中科院分区：
文献类型：
--
作者：
Azzaoui, Kamal;Hamon, Jacques;Urban, Laszlo
本研究描述了一种从大量靶标（体外安全药理学）中获得的结合数据挖掘和建模的方法，以区分混杂和选择性化合物之间的差异。生成了滥交和选择性的两个朴素贝叶斯模型，并在测试集和公开可用的药物数据库上进行了验证。该模型显示，与处于早期开发阶段或在临床开发阶段失败的化合物相比，已上市药物的得分更高（滥交率更低）。这种模型可用于筛选高通量筛选数据或先导物优化。
This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naive Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.