Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Department of Microbiology & Parasitology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Bioorg Med Chem. 2010 Mar 15;18(6):2225-2231. doi: 10.1016/j.bmc.2010.01.068. Epub 2010 Feb 6.
There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
有许多具有不同抗寄生虫药物敏感性特征的病原体寄生虫物种。不幸的是，几乎所有的定量构效关系（QSAR）模型都只能预测一种寄生虫物种对抗寄生虫药物的生物活性。因此，用单一统一模型预测一种药物对不同物种的活性概率是一个非常重要的目标。为此，我们使用马尔可夫链理论来计算新的多目标光谱矩，以拟合一个 QSAR 模型，该模型首次预测了文献中测试的 500 种药物对 16 种寄生虫物种的 mt-QSAR 模型，以及使用光谱矩对文献中未测试的 207 种其他药物的 mt-QSAR 模型。数据通过线性判别分析（LDA）进行处理，将药物分为对不同测试寄生虫物种有活性或无活性。该模型在训练系列中正确地将 358 种活性化合物中的 311 种（86.9%）和 2577 种非活性化合物中的 2328 种（90.3%）分类。整体训练性能为 89.9%。通过外部预测系列对模型进行验证。在这些系列中，该模型正确地将 190 种中的 157 种（82.6%）和 1277 种非活性化合物中的 1151 种（90.1%）分类。整体预测性能为 89.2%。此外，我们还开发了四种类型的非线性人工神经网络（ANN），并与 mt-QSAR 模型进行了比较。改进的 ANN 模型的整体训练性能为 87%。本研究首次尝试在统一框架内，基于光谱矩分析计算药物对不同寄生虫物种的抗寄生虫作用概率。
