College of Information ENgineering, Shanghai Maritime University, China.
Med Chem. 2010 Nov;6(6):388-95. doi: 10.2174/157340610793563983.
Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.
药物与靶蛋白相互作用的研究是基因组药物发现的重要步骤。仅通过实验很难确定化合物-蛋白质相互作用或药物-靶标相互作用。作为补充，使用机器学习或数据挖掘方法的有效预测模型可以提供很大的帮助。在这项研究中，提出了一种基于最近邻算法和一种新的度量标准的预测方法，该度量标准是通过结合化合物相似性和功能域组成获得的。将靶蛋白分为以下几类：酶、离子通道、G 蛋白偶联受体和核受体。结果，建立了四个具有最佳参数的预测器。通过 Jackknife 交叉验证测试评估的四组靶蛋白的总体预测准确率分别为 90.23%、94.74%、97.80%和 97.51%，表明化合物相似性和功能域组成对预测药物-靶标相互作用网络非常有效。
