Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets
Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets
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DOI：
10.1007/s11095-011-0413-x
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发表时间：
2011-08-01
影响因子：
3.7
通讯作者：
Freundlich, Joel S.
中科院分区：
文献类型：
--
作者：
Ekins, Sean;Freundlich, Joel S.
寻找具有抗结核分枝杆菌（Mtb）活性的小分子越来越多地使用高通量筛选和计算方法。从合作药物发现结核病（CDD TB）数据库的几个公共数据集进行了评估与化学信息学的方法，以验证其效用，并建议化合物test.Previously报道的贝叶斯分类模型被用来预测一组283诺华化合物测试对结核分枝杆菌（含需氧和厌氧命中），并搜索FDA批准的药物。Novartis化合物也被计算SMARTS警报过滤，以识别潜在的不良子结构。使用Novartis化合物作为贝叶斯模型的测试集，证明了在随机筛选中发现计算模型中没有的需氧命中物（N = 34）的> 4.0倍富集。在FDA药物数据库中发现Mtb活性化合物时观察到10倍富集。85.9%的诺华化合物未通过雅培SMARTS警报，这一数值远高于已知的结核病药物。来自不同组的SMARTS过滤器的更高水平的失败也与Lipinski违规的数量相关。这些计算方法可能有助于为结核病药物发现找到理想的线索。
The search for small molecules with activity against Mycobacterium tuberculosis (Mtb) increasingly uses high throughput screening and computational methods. Several public datasets from the Collaborative Drug Discovery Tuberculosis (CDD TB) database have been evaluated with cheminformatics approaches to validate their utility and suggest compounds for testing.Previously reported Bayesian classification models were used to predict a set of 283 Novartis compounds tested against Mtb (containing aerobic and anaerobic hits) and to search FDA approved drugs. The Novartis compounds were also filtered with computational SMARTS alerts to identify potentially undesirable substructures.Using the Novartis compounds as a test set for the Bayesian models demonstrated a > 4.0-fold enrichment over random screening for finding aerobic hits not in the computational models (N = 34). A 10-fold enrichment was observed for finding Mtb active compounds in the FDA drugs database. 85.9% of the Novartis compounds failed the Abbott SMARTS alerts, a value substantially higher than for known TB drugs. Higher levels of failures of SMARTS filters from different groups also correlate with the number of Lipinski violations.These computational approaches may assist in finding desirable leads for Tuberculosis drug discovery.