Shaik Basheerulla, Zafar Tabassum, Agrawal Vijay K
Department of Applied Sciences, National Institute of Technical Teachers' Training & Research, Bhopal, Madhya Pradesh 462002, India.
CSIR-Advanced Materials and Processes Research Institute (AMPRI), Bhopal, Madhya Pradesh 462064, India.
Int J Med Chem. 2013;2013:795621. doi: 10.1155/2013/795621. Epub 2013 Dec 30.
The present study deals with the estimation of the anti-HIV activity (log1/C) of a large set of 107 HEPT analogues using molecular descriptors which are responsible for the anti-HIV activity. The study has been undertaken by three techniques MLR, ANN, and SVM. The MLR model fits the train set with R (2)=0.856 while in ANN and SVM with higher values of R (2) = 0.850, 0.874, respectively. SVM model shows improvement to estimate the anti-HIV activity of trained data, while in test set ANN have higher R (2) value than those of MLR and SVM techniques. R m (2) = metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O), and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C) present set of compounds.
本研究使用与抗HIV活性相关的分子描述符，对一大组107种HEPT类似物的抗HIV活性（log1/C）进行了估算。该研究采用了多元线性回归（MLR）、人工神经网络（ANN）和支持向量机（SVM）三种技术。MLR模型对训练集的拟合度为R (2)=0.856，而ANN和SVM对训练集的拟合度更高，R (2)值分别为0.850和0.874。SVM模型在估算训练数据的抗HIV活性方面表现出优势，而在测试集中，ANN的R (2)值高于MLR和SVM技术。R m (2)指标和岭回归分析表明，所提出的由MATS5e、RDF080u、T(O⋯O)和MATS5m这四个变量作为相关描述符的模型，最适合估算当前这组化合物的抗HIV活性（log 1/C）。
