Singh Shalini, Supuran Claudiu T
a QSAR & Cheminformatics Laboratory, Department of Chemistry , Bareilly College , Bareilly , Uttar Pradesh , India and.
b NEUROFARBA Department, Section of Pharmaceutical Chemistry, Universita degli Studi di Firenze , Polo Scientifico, Sesto Fiorentino (Florence) , Italy.
J Enzyme Inhib Med Chem. 2016;31(3):417-24. doi: 10.3109/14756366.2015.1031127. Epub 2015 May 6.
A quantitative structure-activity relationship (QSAR) study of sulfonamide inhibitors targeting the β-carbonic anhydrase (CA, EC 4.2.1.1) from the fungus Malassezia globosa is reported. A large set of PRECLAV descriptors has been used to obtain four parametric models. This study presents QSAR data on a pool of 28 compounds. The quality of prediction is high enough (SE = 0.3446, r(2) = 0.8687, F = 39.6921, Q = 0.7446). A heuristic algorithm selected the best multiple linear regression (MLR) equation which showed the correlation between the observed values and the calculated values of activity. The proposed prediction set included new, not yet synthesized, 23 molecules having various structures. Many compounds in the prediction set seem to possess higher computed activity compared to the presently available M. globosa β-CA inhibitors.
报道了针对球形马拉色菌β-碳酸酐酶（CA，EC 4.2.1.1）的磺酰胺抑制剂的定量构效关系（QSAR）研究。使用了大量的PRECLAV描述符来获得四个参数模型。本研究给出了28种化合物的QSAR数据。预测质量足够高（SE = 0.3446，r(2) = 0.8687，F = 39.6921，Q = 0.7446）。一种启发式算法选择了最佳的多元线性回归（MLR）方程，该方程显示了活性观测值与计算值之间的相关性。所提出的预测集包括23个具有各种结构的尚未合成的新分子。与目前可用的球形马拉色菌β-CA抑制剂相比，预测集中的许多化合物似乎具有更高的计算活性。
