In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug–target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.

中文翻译：

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使用拉普拉斯正则化 Schatten p-Norm 最小化从头预测药物-靶标相互作用

在制药科学中，药物发现的一个关键步骤是识别药物-靶标相互作用 (DTI)。然而，只有一小部分 DTI 已通过实验验证。此外，通过传统的生化实验来捕捉药物和靶标之间的新相互作用是一个极其费力、昂贵和耗时的过程。因此，设计预测潜在相互作用的计算方法来指导实验验证具有实际意义，特别是对于从头的情况。在本文中，我们提出了一种新的算法，即拉普拉斯正则化 Schatten p-范数最小化（LRSpNM），预测新药的潜在靶蛋白和没有已知相互作用的新靶点的潜在药物。具体来说，我们首先利用药物和靶点的相似性信息来动态预填充部分未知的相互作用。然后基于交互矩阵是低秩的假设，我们使用 Schatten p-范数最小化模型与拉普拉斯正则化项相结合，以提高新药/目标病例的预测性能。最后，我们通过一种高效的交替方向乘法器算法对 LRSpNM 模型进行数值求解。我们在五个数据集上评估 LRSpNM，大量的数值实验表明，与五种最先进的 DTI 预测算法相比，LRSpNM 实现了更好、更稳健的性能。此外，我们针对新药和新靶点预测进行了两个案例研究，这说明 LRSpNM 可以成功预测大多数经过实验验证的 DTI。