Identification of drug–target interactions (DTIs) is critical for discovering potential target protein candidates for new drugs. However, traditional experimental methods have limitations in discovering DTIs. They are time‐consuming, tedious, and expensive, and often suffer from high false‐positive rates and false‐negative rates. Therefore, using computational methods to predict DTIs has received extensive attention from many researchers in recent years. To address this issue, in this paper, an effective prediction model is presented which is based on the information of drug molecular structure data and protein sequence data. It performs prediction with the following procedures. First, we transform the sequences of each target into a position‐specific scoring matrix (PSSM), such that the features can retain biological evolutionary information. We then use a feature vector of molecular substructure fingerprints to describe the chemical structure information of the drug compounds. Second, the Legendre moments algorithm is used to extract new features from the PSSM. Finally, a classification algorithm called rotation forest is used to perform prediction, we tested its prediction performance on four golden standard data sets: enzymes, G‐protein‐coupled receptors, ion channels, and nuclear receptors. As a result, the proposed method achieves average accuracies of 0.9026, 0.8260, 0.8703, and 0.7444 on these four data sets using five‐fold cross‐validation. We also compare the proposed method with the support vector machine and other existing approaches. The proposed model is proved to be superior to comparative methods, showing that it is feasible, effective, and robust for predicting potential DTI.

中文翻译：

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从蛋白质序列和药物亚结构指纹信息预测药物-靶标相互作用的计算方法

药物-靶标相互作用 (DTI) 的鉴定对于发现新药的潜在靶蛋白候选物至关重要。然而，传统的实验方法在发现 DTI 方面存在局限性。它们耗时、乏味且昂贵，并且经常遭受高假阳性率和假阴性率的困扰。因此，近年来利用计算方法来预测 DTI 受到了许多研究人员的广泛关注。针对这一问题，本文提出了一种基于药物分子结构数据和蛋白质序列数据信息的有效预测模型。它通过以下过程执行预测。首先，我们将每个目标的序列转换为特定位置的评分矩阵（PSSM），以便特征可以保留生物进化信息。然后我们使用分子亚结构指纹的特征向量来描述药物化合物的化学结构信息。其次，使用勒让德矩算法从 PSSM 中提取新特征。最后，使用称为旋转森林的分类算法进行预测，我们在四个黄金标准数据集上测试了其预测性能：酶、G 蛋白偶联受体、离子通道和核受体。因此，所提出的方法使用五重交叉验证在这四个数据集上实现了 0.9026、0.8260、0.8703 和 0.7444 的平均准确率。我们还将所提出的方法与支持向量机和其他现有方法进行了比较。所提出的模型被证明优于比较方法，表明它是可行的、有效的、