College of Mathematics and Computer Science, Shanxi Normal University, Linfen, 041004, China.
College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350106, Fujian, China.
J Mol Model. 2020 Feb 15;26(3):60. doi: 10.1007/s00894-020-4315-x.
Due to rising development costs and stagnant product outputs of traditional drug discovery methods, drug repositioning, which discovers new indications for existing drugs, has attracted increasing interest. Computational drug repositioning can integrate prioritization information and accelerate time lines even further. However, most existing methods for predicting drug repositioning have low precisions. The present article proposed a new method named DDAPRED (https://github.com/nongdaxiaofeng/DDAPRED) for drug repositioning prediction. The method integrated multiple sources of drug similarity and disease similarity information, and it used the regularized logistic matrix decomposition method to significantly improve the prediction performance. In 5-fold cross-validation, the areas under the receiver operating characteristic curve (AUROC) and the precision-recall curve (AUPRC) of DDAPRED reached 0.932 and 0.438, respectively, exceeding other methods. The present study also analyzed the parameters influencing the model performance and the effect of different drug similarity information in-depth, and it verified the treatment relationship of the top 50 predictions with unknown relationships in the training set, further demonstrating the practicability of our method.
由于传统药物发现方法的开发成本不断上升和产品产出停滞不前，药物重定位（即发现现有药物的新适应症）引起了越来越多的关注。计算药物重定位可以整合优先级信息，并进一步加速时间线。然而，大多数现有的药物重定位预测方法的精度都较低。本文提出了一种名为 DDAPRED（https://github.com/nongdaxiaofeng/DDAPRED）的新方法，用于药物重定位预测。该方法整合了多种药物相似性和疾病相似性信息来源，并使用正则化逻辑矩阵分解方法显著提高了预测性能。在 5 折交叉验证中，DDAPRED 的接收器操作特征曲线（AUROC）和精度-召回曲线（AUPRC）的面积分别达到 0.932 和 0.438，优于其他方法。本研究还深入分析了影响模型性能的参数和不同药物相似性信息的影响，并验证了训练集中未知关系的前 50 个预测的治疗关系，进一步证明了我们方法的实用性。
