A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

中文翻译：

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基于综合相似性和半监督学习的药物相互作用预测

药物-药物相互作用 (DDI) 定义为两种药物之间的关联，其中一种药物的药理作用受到另一种药物的影响。阳性DDI通常可以提高患者的治疗效果，而阴性DDI是引起药物不良反应的主要原因，甚至导致药物退出市场和患者死亡。因此，识别 DDI 已成为药物开发和疾病治疗的关键组成部分。在这项研究中，我们提出了一种基于集成相似性和半监督学习 (DDI-IS-SL) 来预测 DDI 的新方法。DDI-IS-SL整合药物化学、生物学和表型数据，利用余弦相似度法计算药物的特征相似度。药物的 Gaussian Interaction Profile 核相似性也是基于已知的 DDI 计算的。使用半监督学习方法（Regularized Least Squares 分类器）来计算药物-药物对的相互作用可能性分数。在 5 折交叉验证、10 折交叉验证和从头药物验证方面，DDI-IS-SL 可以实现比其他比较方法更好的预测性能。此外，DDI-IS-SL 的平均计算时间比其他比较方法短。最后，案例研究进一步证明了 DDI-IS-SL 在实际应用中的性能。DDI-IS-SL 可以实现比其他比较方法更好的预测性能。此外，DDI-IS-SL 的平均计算时间比其他比较方法短。最后，案例研究进一步证明了 DDI-IS-SL 在实际应用中的性能。DDI-IS-SL 可以实现比其他比较方法更好的预测性能。此外，DDI-IS-SL 的平均计算时间比其他比较方法短。最后，案例研究进一步证明了 DDI-IS-SL 在实际应用中的性能。