MSPEDTI: Prediction of Drug–Target Interactions via Molecular Structure with Protein Evolutionary Information

SIMPLE SUMMARY: Drug discovery is the process of identifying potential new compounds through biological, chemical, and pharmacological means. Billions of dollars are spent each year on research aimed at discovering, designing, and developing new drugs for a wide range of diseases. However, the research and development of new drugs remain time-consuming and sometimes difficult to complete. With the development of new experimental techniques, huge amounts of data are generated at different stages of drug development. Biomedical research, especially in the field of drug discovery, is currently undergoing a major shift towards “big data” applications of artificial intelligence technologies. Therefore, a key challenge for future drug discovery research is the development of robust artificial-intelligence-based predictive tools for drug–target interactions (DTIs) that can study biomedical problems from multiple perspectives. In this study, a deep-learning-based prediction model for DTIs was designed by combining information on drug structure and protein evolution to provide theoretical support for drug research. ABSTRACT: The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug–target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.