Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
BMC Bioinformatics. 2021 Jul 24;22(1):385. doi: 10.1186/s12859-021-04298-y.
Polypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects.
We propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug-protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision-Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method.
The performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS .
多药疗法是一种同时使用多种药物的治疗方法。药物同时使用时可能会相互作用。因此，了解和减轻多药疗法的副作用对于患者的安全和健康至关重要。由于已知的多药疗法副作用很少，并且在临床试验中未检测到，因此开发了计算方法来模拟多药疗法的副作用。
我们提出了一种基于神经网络的多药疗法副作用预测方法（NNPS），该方法使用基于单药副作用和药物-蛋白相互作用信息的新型特征向量。所提出的方法快速高效，允许研究大量的多药疗法副作用。我们的新颖之处在于为药物定义新的特征向量，并将其与神经网络架构相结合，应用于多药疗法副作用预测的背景。我们在基准数据集上比较了 NNPS 与 5 种成熟方法预测 964 种多药疗法副作用的效果，结果表明 NNPS 在准确性、复杂性和运行时间速度方面均优于所有 5 种方法的结果。NNPS 在 5 倍交叉验证中，在接收器操作特征曲线下面积方面的表现优于其他 5 种方法中的最佳算法（Decagon 方法）约 9.2%，在精度方面的表现优于其他 5 种方法中的最佳算法约 10.3%，在召回率方面的表现优于其他 5 种方法中的最佳算法约 12.8%，在 F1 分数方面的表现优于其他 5 种方法中的最佳算法约 8.6%，在 Matthews 相关系数方面的表现优于其他 5 种方法中的最佳算法约 18.7%。此外，通过 NNPS 方法，Decagon 方法的 1 折交叉验证运行时间（15 天）缩短到 8 小时。
我们将 NNPS 的性能与 5 种成熟的方法（Decagon、串联药物特征、Deep Walk、DEDICOM 和 RESCAL）进行了基准测试，以预测 964 种多药疗法副作用。我们采用 5 倍交叉验证进行 50 次迭代，并使用结果的平均值来评估 NNPS 方法的性能。通过与 5 种成熟方法在准确性、复杂性和运行时间速度方面进行评估，该方法在药理学中的一个重要而具有挑战性的问题上表现出良好的性能。NNPS 算法的数据集和代码可在 https://github.com/raziyehmasumshah/NNPS 上免费获取。
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