Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.
Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.
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DOI：
10.1186/1471-2210-12-1
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发表时间：
2012-03-31
期刊：
影响因子：
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通讯作者：
Scaria V
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文献类型：
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作者：
Periwal V;Kishtapuram S;Open Source Drug Discovery Consortium;Scaria V
耐多药结核病在世界各地以大流行的形式出现，以及结核病新疗法的缺乏，再次表明有必要加快发现具有抗结核活性的新分子。虽然抗结核活性的高通量筛查是可用的，但它们昂贵，繁琐且耗时，无法大规模进行。因此，为了节省成本和时间，仍然需要优先考虑用于生物筛选的分子。包括机器学习在内的计算方法已被广泛用于构建高通量虚拟屏幕的分类器，以优先考虑分子以进行进一步分析。基于公共领域的高通量生物筛选或分析的数据集的可用性使得计算方法成为构建预测模型的合理主张。此外，这种方法将大大节省运行高吞吐量屏幕所需的成本、精力和时间。我们表明，通过使用四个有监督的最先进分类器（SMO，随机森林，朴素贝叶斯和J48），我们能够在具有合理的AROC（0.6-0.75）和BCR（60-66%）值的极度不平衡（少数类比率：0.6%）的抗结核分子大数据集上生成计算机预测模型。此外，这些模型能够提供3-4倍的富集比随机选择。在本研究中，我们使用了来自公共领域高通量筛选的体外抗结核活性筛选数据，基于分子描述符构建了高度准确的分类器。我们表明，机器学习工具可用于为虚拟高通量筛选构建高效的预测模型，以优先考虑大型分子库中的分子。
The emergence of Multi-drug resistant tuberculosis in pandemic proportions throughout the world and the paucity of novel therapeutics for tuberculosis have re-iterated the need to accelerate the discovery of novel molecules with anti-tubercular activity. Though high-throughput screens for anti-tubercular activity are available, they are expensive, tedious and time-consuming to be performed on large scales. Thus, there remains an unmet need to prioritize the molecules that are taken up for biological screens to save on cost and time. Computational methods including Machine Learning have been widely employed to build classifiers for high-throughput virtual screens to prioritize molecules for further analysis. The availability of datasets based on high-throughput biological screens or assays in public domain makes computational methods a plausible proposition for building predictive models. In addition, this approach would save significantly on the cost, effort and time required to run high throughput screens. We show that by using four supervised state-of-the-art classifiers (SMO, Random Forest, Naive Bayes and J48) we are able to generate in-silico predictive models on an extremely imbalanced (minority class ratio: 0.6%) large dataset of anti-tubercular molecules with reasonable AROC (0.6-0.75) and BCR (60-66%) values. Moreover, these models are able to provide 3-4 fold enrichment over random selection. In the present study, we have used the data from in-vitro screens for anti-tubercular activity from a high-throughput screen available in public domain to build highly accurate classifiers based on molecular descriptors of the molecules. We show that Machine Learning tools can be used to build highly effective predictive models for virtual high-throughput screens to prioritize molecules from large molecular libraries.