Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Eur J Med Chem. 2009 Nov;44(11):4516-21. doi: 10.1016/j.ejmech.2009.06.018. Epub 2009 Jun 24.
There are many of pathogen bacteria species which very different susceptibility profile to different antibacterial drugs. There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Bacteria Pairs (DBPs) of affinity/non-affinity drugs with similar/dissimilar bacteria and represented it as a large network, which may be used to identify drugs that can act on bacteria. Computational chemistry prediction of the biological activity based on one-target Quantitative Structure-Activity Relationship (ot-QSAR) studies substantially increases the potentialities of this kind of networks avoiding time and resource consuming experiments. Unfortunately almost all ot-QSAR models predict the biological activity of drugs against only one bacterial species. Consequently, multi-tasking learning to predict drug's activity against different species with a single model (mt-QSAR) is a goal of major importance. These mt-QSARs offer a good opportunity to construct drug-drug similarity Complex Networks. Unfortunately, almost QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multi-bacteria QSAR classification model. The model correctly classifies 202 out of 241 active compounds (83.8%) and 169 out of 200 non-active cases (84.5%). Overall training predictability was 84.13% (371 out of 441 cases). The validation of the model was carried out by means of external predicting series, classifying the model 197 out of 221 (89.4%) cases. In order to show how the model functions in practice a virtual screening was carried out recognizing the model as active 86.7%, 520 out of 600 cases not used in training or predicting series. Outputs of this QSAR model were used as inputs to construct a network. The observed network has 1242 nodes (DBPs), 772,736 edges or DBPs with similar activity (sDBPs). The network predicted has 1031 nodes, 641,377 sDBPs. After edge-to-edge comparison, we have demonstrated that the predicted network is significantly similar to the observed one and both have distribution closer to exponential than to normal.
有许多病原体细菌物种对不同的抗菌药物具有不同的敏感性。有许多药物被描述为与大量受体具有非常不同的亲和力。在这项工作中，我们选择了具有相似/不同细菌的亲和/非亲和药物的药物-细菌对（DBP），并将其表示为一个大型网络，该网络可用于识别可作用于细菌的药物。基于单靶定量构效关系（ot-QSAR）研究的计算化学预测大大提高了这种网络的潜力，避免了耗时且资源密集型的实验。不幸的是，几乎所有 ot-QSAR 模型都仅预测一种细菌物种的药物的生物活性。因此，使用单个模型预测药物对不同物种的活性（mt-QSAR）是一个非常重要的目标。这些 mt-QSAR 为构建药物-药物相似性复杂网络提供了很好的机会。不幸的是，几乎没有 QSAR 模型是特异性的，或者仅预测对一种受体的活性。为了解决这个问题，我们在这里开发了一种多细菌 QSAR 分类模型。该模型正确分类了 241 种活性化合物中的 202 种（83.8％）和 200 种非活性化合物中的 169 种（84.5％）。整体训练预测率为 84.13％（371 例中的 441 例）。通过外部预测系列验证了该模型，将 221 例中的 197 例（89.4％）进行分类。为了展示模型的实际应用，我们进行了虚拟筛选，将模型识别为活性的 600 例中的 520 例（86.7％），这些案例未用于训练或预测系列。该 QSAR 模型的输出用作构建网络的输入。所观察到的网络具有 1242 个节点（DBP），772736 个边或具有相似活性的 DBP（sDBP）。预测的网络具有 1031 个节点，641377 个 sDBP。在边到边的比较之后，我们证明了预测的网络与观察到的网络非常相似，并且两者的分布都更接近指数而不是正态分布。
