A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction.

Jiacheng Sun,Jianping Chen,Hongjie Wu,You Lu,Linqian Cui,Qiming Fu

doi:10.3389/fcell.2022.794413

Jiacheng Sun, Jianping Chen + Show 4 more

Open Access

https://doi.org/10.3389/fcell.2022.794413

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Abstract

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).

Highlights

Weight Allocation Based on Q-Learning addition, protein-protein interactions play a key role in many biological processes (Guo et al, 2015), leading to the emergence of large-scale experimental data on genes and proteins, making drug discovery and repositioning in biomedical research more difficult (Ding et al, 2019a)
Previous studies have shown that drug-target interactions (DTIs) prediction based on experimental verification can effectively predict some novel interactions between drugs and targets, and the computational methods used to predict DTIs can significantly improve the efficiency of drug discovery
We propose a model for optimizing weight allocation of heterogeneous data based on Q-learning algorithm to improve the accuracy of DTIs prediction

Summary

Introduction

Diseases are usually caused by defective proteins in the body or the functional structure of viral proteins. The purpose of DTIs prediction is to identify potential novel drugs or novel targets for existing drugs, and provide a list of candidate drugs for drug discovery, greatly improving the efficiency of research and development and reducing the cost of experiments. Existing methods predict DTIs mainly based on a small number of experimentally validated interactions in existing databases, such as DrugBank (Wishart et al, 2008), KEGG DRUG (Kanehisa et al, 2012), and SuperTarget (Günther et al, 2008). Previous studies have shown that DTIs prediction based on experimental verification can effectively predict some novel interactions between drugs and targets, and the computational methods used to predict DTIs can significantly improve the efficiency of drug discovery

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