The aim of this paper is to develop a system for drug–target interaction prediction using artificial intelligence which involves development of both machine learning and deep learning-based systems. In this paper, we use a convolutional neural network (CNN) model, to classify drug–target interactions between drug pairs. Applied to the DDI-Corpus dataset, the single CNN model achieve performance with an F1-score of 0.82 ± 0.012 for the single model and 0.81 ± 0.015 for the ensemble model using deep learning-based CNN with an approved accuracy of 96.72% which is an extra-ordinary achievement. This work has also been performed using the machine learning-based classifiers support vector machine (SVM). For machine learning-based implementation, drug-bank dataset was used for the training and testing. The main challenge when using machine learning for this purpose is the availability of negative DTI to train on. Training machine learning model, the SVM achieved an area under the ROC curve (AUC) of 0.753 ± 0.006, which taking the difference in computational resources into consideration compares well to the AUC of 0.886 ± 0.010 network-based state-of-the-art approach. We achieved and best accuracy of 93.76% using SVM after testing several times.

本文的目的是开发一种使用人工智能进行药物-靶标相互作用预测的系统，该系统涉及机器学习和基于深度学习的系统的开发。在本文中，我们使用卷积神经网络 (CNN) 模型对药物对之间的药物-靶标相互作用进行分类。应用于 DDI-Corpus 数据集，单个 CNN 模型实现了性能，单个模型的 F1 分数为 0.82 ± 0.012，使用基于深度学习的 CNN 的集成模型的 F1 分数为 0.81 ± 0.015，批准的准确率为 96.72%，即一项非凡的成就。还使用基于机器学习的分类器支持向量机 (SVM) 执行了这项工作。对于基于机器学习的实施，药物银行数据集用于训练和测试。为此目的使用机器学习时的主要挑战是可用于训练的负 DTI。训练机器学习模型，SVM 实现了 ROC 曲线下面积 (AUC) 0.753 ± 0.006，考虑到计算资源的差异，与基于网络的最新技术的 AUC 0.886 ± 0.010 相当方法。经过多次测试，我们使用 SVM 实现了 93.76% 的最佳准确率。