Silva Daniel Gedder, Rocha Josmar Rodrigues, Sartori Geraldo Rodrigues, Montanari Carlos Alberto
a Grupo de Química Medicinal , Instituto de Química de São Carlos, Universidade de São Paulo , São Carlos - SP 13566-590 , Brazil.
J Biomol Struct Dyn. 2017 Nov;35(15):3232-3249. doi: 10.1080/07391102.2016.1252282. Epub 2016 Nov 28.
The HQSAR, molecular docking, and ROCS were applied to a data-set of 57 cruzain inhibitors. The best HQSAR model (q = .70, r = .95, [Formula: see text] = .62, [Formula: see text] = .09 and [Formula: see text] = .26), employing well-balanced, diverse training (40) and test (17) sets, was obtained using atoms (A), bonds (B), and hydrogen (H) as fragment distinctions and 6-9 as fragment sizes. This model was then used to predict the unknown potencies of 121 compounds (the V1 database), giving rise to a satisfactory predictive r value of .65 (external validation). By employing an extra external data-set comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC score well over .70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The area under the curve (AUC) obtained with the ROC curves (ROC AUC) was .72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC values of these new compounds. Some compounds identified using this method have shown calculated potencies higher than those which have originated them.
将HQSAR、分子对接和ROCS应用于57种克沙因抑制剂的数据集。使用原子（A）、键（B）和氢（H）作为片段区分且片段大小为6 - 9，通过平衡、多样的训练集（40个）和测试集（17个），获得了最佳的HQSAR模型（q = 0.70，r = 0.95，[公式：见原文] = 0.62，[公式：见原文] = 0.09且[公式：见原文] = 0.26）。然后使用该模型预测121种化合物（V1数据库）的未知效价，得到了令人满意的预测r值0.65（外部验证）。通过使用从ChEMBL或CDD数据库检索的包含1223种化合物的额外外部数据集（V3数据库），获得了总体ROC AUC得分远高于0.70的结果。用最佳HQSAR模型（模型3.4）获得的贡献图与预测的结合模式以及所研究化合物的生物学效价一致。我们还使用ROCS方法筛选了这些化合物，ROCS是一种能够快速识别与查询分子匹配形状的高斯形状体积过滤器。ROC曲线得到的曲线下面积（AUC）为0.72，表明该方法在区分活性和非活性克沙因抑制剂方面非常有效。这组信息引导我们提出了有待合成的新型克沙因抑制剂。然后，使用获得的最佳HQSAR模型预测这些新化合物的pIC值。使用该方法鉴定出的一些化合物显示出计算得出的效价比产生它们的化合物更高。
