Sun Jiacheng, Lu You, Cui Linqian, Fu Qiming, Wu Hongjie, Chen Jianping
School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China.
Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China.
Front Cell Dev Biol. 2022 Mar 4;10:794413. doi: 10.3389/fcell.2022.794413. eCollection 2022.
Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge. In this paper, we propose a model based on Q-learning algorithm and Neighborhood Regularized Logistic Matrix Factorization (QLNRLMF) to predict DTIs. First, we obtain three different drug-drug similarity matrices and three different target-target similarity matrices by using different similarity calculation methods based on heterogeneous data, including drug chemical structure, target protein sequence and drug-target interactions. Then, we initialize a set of weights for the drug-drug similarity matrices and target-target similarity matrices respectively, and optimize them through Q-learning algorithm. When the optimal weights are obtained, a new drug-drug similarity matrix and a new drug-drug similarity matrix are obtained by linear combination. Finally, the drug target interaction matrix, the new drug-drug similarity matrices and the target-target similarity matrices are used as inputs to the Neighborhood Regularized Logistic Matrix Factorization (NRLMF) model for DTIs. Compared with the existing six methods of NetLapRLS, BLM-NII, WNN-GIP, KBMF2K, CMF, and NRLMF, our proposed method has achieved better effect in the four benchmark datasets, including enzymes(E), nuclear receptors (NR), ion channels (IC) and G protein coupled receptors (GPCR).
计算和预测药物 - 靶点相互作用（DTIs）是新型药物发现领域的关键步骤。如今，许多模型通过融合异构信息（如药物化学结构和靶点蛋白质序列等）提高了DTIs的预测性能。然而，在融合过程中，如何合理分配异构信息的权重是一个巨大挑战。本文提出一种基于Q学习算法和邻域正则化逻辑矩阵分解（QLNRLMF）的模型来预测DTIs。首先，我们基于异构数据（包括药物化学结构、靶点蛋白质序列和药物 - 靶点相互作用）使用不同的相似性计算方法，获得三个不同的药物 - 药物相似性矩阵和三个不同的靶点 - 靶点相似性矩阵。然后，我们分别为药物 - 药物相似性矩阵和靶点 - 靶点相似性矩阵初始化一组权重，并通过Q学习算法对其进行优化。当获得最优权重时，通过线性组合得到一个新的药物 - 药物相似性矩阵和一个新的靶点 - 靶点相似性矩阵。最后，将药物 - 靶点相互作用矩阵、新的药物 - 药物相似性矩阵和靶点 - 靶点相似性矩阵作为输入，用于DTIs的邻域正则化逻辑矩阵分解（NRLMF）模型。与现有的六种方法NetLapRLS、BLM - NII、WNN - GIP、KBMF2K、CMF和NRLMF相比，我们提出的方法在包括酶（E）、核受体（NR）、离子通道（IC）和G蛋白偶联受体（GPCR）的四个基准数据集上取得了更好的效果。
