Prado-Prado Francisco J, Borges Fernanda, Uriarte Eugenio, Peréz-Montoto Lazaro G, González-Díaz Humberto
Physic-Chemical Molecular Research Units, Department of Organic Chemistry, Faculty of Pharmacy, University of Porto, 4150-047 Porto, Portugal.
Anal Chim Acta. 2009 Oct 5;651(2):159-64. doi: 10.1016/j.aca.2009.08.022. Epub 2009 Aug 25.
The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.
如今，抗病毒定量构效关系（QSAR）模型存在一个重要局限性。它们只能预测药物针对单一病毒种类的生物活性。这是由以下事实决定的：当前报道的大多数分子描述符仅编码有关分子结构的信息。因此，用一个统一模型预测药物对不同病毒种类具有活性的概率是一个极其重要的目标。在这项工作中，我们使用马尔可夫链理论来计算新的多靶点光谱矩，以拟合针对40种病毒种类具有活性的药物的QSAR模型。该模型基于最近文献中作为抗病毒剂测试的500种药物（包括活性和非活性化合物）；并非所有药物都针对所有病毒进行了预测，而只是针对那些有实验值的药物。该数据库还包含《默克索引》中报道的207种知名化合物（不像之前那些那么新），它们具有其他活性，但不包括针对任何病毒种类的抗病毒作用。我们使用线性判别分析（LDA）将所有这些药物分为两类，即针对我们处理其数据的不同测试病毒种类的活性或非活性药物。该模型正确地将5594种非活性化合物中的5129种（91.69%）和422种活性化合物中的412种（97.63%）进行了分类。总体训练可预测性为92.34%。通过外部预测系列对模型进行验证，该模型对2779种非活性化合物中的2568种和229种活性化合物中的224种进行了分类。总体训练可预测性为92.82%。本工作首次尝试在一个统一框架内基于光谱矩分析计算抗病毒药物针对不同病毒种类的概率。
