Department of Chemistry, Islamic Azad University-Mahshahr Branch, Mahshahr, Iran.
J Comput Chem. 2010 Sep;31(12):2354-62. doi: 10.1002/jcc.21529.
Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88).
建立了 107 个 1-[(2-羟乙氧基)甲基]-6-(苯硫基)胸苷类似物的定量构效关系模型，这些类似物是一种有效的 HIV-1 逆转录酶抑制剂。采用多元线性回归（MLR）技术研究了这些化合物的活性。应用了一种称为Memorized_ACS 的蚁群优化算法来选择相关描述符和检测异常值。该算法使用基于前几次迭代知识整合的外部记忆。首先，内存为空，然后通过运行几个 ACS 算法来填充。在这方面，在每次 ACS 运行后，精英蚂蚁被存储在内存中，然后继续填充内存。在这里，所有收集在内存中的精英蚂蚁都会进行信息素更新，这会提高 ACS 算法的探索和利用行为。然后，内存被清空，再次使用更新后的信息素轨迹运行几个 ACS 算法来填充。这个过程重复进行几次迭代。最后，内存中包含了几个针对该问题的最佳解决方案。描述符在外部内存中的出现次数是其重要性的一个很好的标准。最后，通过精英蚂蚁进行预测，并通过考虑每个描述符的重要性来进行解释。最佳的 MLR 模型具有 0.47 log(1/EC(50))个单位的训练误差（R(2) = 0.90）和 0.76 log(1/EC(50))个单位的预测误差（R(2) = 0.88）。
