The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential DTIs. The performance of DTI prediction depends heavily on the features extracted from drugs and target proteins. The existing predictors vary in input information and each has its own advantages. Therefore, combining the advantages of individual models and generating high-quality representations for drug-target pairs are effective ways to improve the performance of DTI prediction. In this study, we exploit both biochemical characteristics of drugs via network integration and molecular sequences via word embeddings, then we develop an ensemble model, KenDTI, based on two types of methods, i.e., network-based and classification-based. We assess the performance of KenDTI on two large-scale datasets, The experimental results show that KenDTI outperforms the state-of-the-art DTI predictors by a large margin. Moreover, KenDTI is robust against missing data in input networks and lack of prior knowledge. It is able to predict for drug-candidate chemical compounds with scarce information.

中文翻译：

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KenDTI：通过集成多源信息预测药物-靶点相互作用的集成模型


药物与靶标相互作用（DTI）的识别是药物发现过程中的重要步骤。由于实验验证成本高且成功率低，因此已利用各种计算模型来推断潜在的 DTI。 DTI 预测的性能在很大程度上取决于从药物和目标蛋白中提取的特征。现有的预测器输入信息各异，各有优点。因此，结合各个模型的优点并生成药物-靶标对的高质量表示是提高 DTI 预测性能的有效方法。在本研究中，我们通过网络集成利用药物的生化特征，通过词嵌入利用分子序列，然后我们基于两种类型的方法（即基于网络和基于分类的方法）开发了一个集成模型 KenDTI。我们在两个大型数据集上评估了 KenDTI 的性能，实验结果表明 KenDTI 大幅优于最先进的 DTI 预测器。此外，KenDTI 对于输入网络中丢失数据和缺乏先验知识具有很强的鲁棒性。它能够在信息稀缺的情况下预测候选药物化合物。