Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Sci Rep. 2023 Mar 3;13(1):3594. doi: 10.1038/s41598-023-30026-y.
Drug-target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug-target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.
药物-靶点相互作用预测是药物开发的一个重要阶段，涉及许多方法。基于临床疗法来识别这些关系的实验方法耗时、昂贵、费力且复杂，引入了许多挑战。一类新方法称为计算方法。在总成本和时间方面，开发更准确的新计算方法可能优于实验方法。在本文中，提出了一种新的药物-靶点相互作用（DTI）预测计算模型，该模型由三个阶段组成，包括特征提取、特征选择和分类。在特征提取阶段，从蛋白质序列中提取不同的特征，如 EAAC、PSSM 等，从药物中提取指纹特征。然后将这些提取的特征进行组合。在下一步中，由于提取的数据量很大，应用了一种名为 IWSSR 的包装式特征选择方法。然后将选择的特征提供给旋转森林分类器，以进行更有效的预测。实际上，我们工作的创新之处在于我们提取了不同的特征；然后使用 IWSSR 选择特征。基于金标准数据集（酶、离子通道、G 蛋白偶联受体、核受体）的十折交叉验证的旋转森林分类器的准确性如下：98.12、98.07、96.82 和 95.64。实验结果表明，所提出的模型在 DTI 预测中具有可接受的准确率，并且与其他论文中提出的方法兼容。
