A novel deep neural network technique for drug-target interaction prediction

Abstract

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. Prediction of drug-target interaction is an essential part of the DD process because it can accelerate it and reduce required costs. DTI prediction performed in silico have used approaches based on molecular docking simulation, similarity-based and network and graph based. This paper presents MPS2ITDTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for representing/encoding molecule and protein sequences into images; and the definition of a deep-learning approach based on a convolutional neuralnetwork in order to create a new method for DTI prediction. The results of this research indicate that the image-based representation of molecule and protein sequences is a viable alternative to the NLP-based approaches and, as such, does not adopt an embedding layer in the neural network. The training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. Regarding the Davis dataset, the results of the experiments indicate a concordance index (CI) of 0.876 and a MSE of 0.276; with the KIBA dataset, 0.836 and 0.226, respectively. Finally, the experimental results utilizing the BindingDB dataset and six core proteins of SARS-CoV-2 suggest that MPS2IT-DTI performs comparably with state-of-the-art methodologies for the repurposing of clinically approved antiviral agents in the context of COVID-19 treatment.