CAS-MPG Partner Institute of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
PLoS One. 2010 Mar 11;5(3):e9603. doi: 10.1371/journal.pone.0009603.
Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.
METHODS/PRINCIPAL FINDINGS: To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.
CONCLUSION/SIGNIFICANCE: Our results indicate that the network prediction system thus established is quite promising and encouraging.
药物-靶标相互作用网络的研究是药物开发的一个重要课题。仅通过实验来确定化合物-蛋白相互作用或潜在的药物-靶标相互作用既耗时又昂贵。作为补充，计算预测方法可以及时为我们提供非常有用的信息。
方法/主要发现：为了实现这一点，药物化合物用官能团编码，蛋白质用包括生化和物理化学性质在内的生物特征编码。采用最大相关性最小冗余 (mRMR) 方法进行最优特征选择程序。目标蛋白不是作为一个整体家族进行分类，而是分为四类：酶、离子通道、G 蛋白偶联受体和核受体。因此，使用最近邻算法作为其操作引擎，建立了四个独立的预测器，分别用于预测药物与这四个蛋白组中的一个之间的相互作用。结果，四个预测器的 jackknife 交叉验证测试的总体成功率分别为 85.48%、80.78%、78.49%和 85.66%。
结论/意义：我们的结果表明，由此建立的网络预测系统非常有前途和令人鼓舞。
