Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.

中文翻译：

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基于矩阵分解的药物再利用预测技术。

经典的药物设计方法成本高昂且耗时，大约 85% 的新提议分子在 FDA 药物批准过程的前三个阶段失败。因此，利用计算方法为已经批准的药物寻找替代适应症的策略至关重要。我们之前展示了非负矩阵三因式分解的功效，该方法允许利用数据集成和机器学习来推断已批准药物的新适应症。在这项工作中，我们提出了 NMTF 方法的创新增强，该方法包括使用蛋白质-蛋白质相互作用网络对药物-蛋白质对的最短路径评估。这种方法允许推断以前从未被视为药物靶点的新蛋白质靶点，增加馈入 NMTF 方法的信息。事实上，这一新进展使以药物为中心的预测研究成为可能，同时识别与特定药物相关的治疗类别、蛋白质靶标和疾病。为了测试我们的方法，我们将 NMTF 和最短路径增强方法应用于过时的数据集合，并将预测与最新版本进行比较，获得了非常好的性能，平均精度得分为 0.82。数据增强策略允许将假定蛋白质目标的数量从 3,691 增加到 15,295，同时该方法的预测性能略有提高。最后，我们还根据文献验证了我们得分最高的预测，找到了药物和蛋白质靶标之间预测相互作用的相关确认，