Open Access

ARTICLE

Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

G. Kavipriya^*, D. Manjula

Department of Computer Science and Engineering, Anna University, Chennai, 600025, India

* Corresponding Author: G. Kavipriya. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1675-1689. https://doi.org/10.32604/iasc.2023.027670

Received 23 January 2022; Accepted 21 March 2022; Issue published 19 July 2022

Abstract

Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this motivation, this paper presents a new drug target interaction prediction using optimal recurrent neural network (DTIP-ORNN) technique. The goal of the DTIP-ORNN technique is to predict the DTIs in a semi-supervised way, i.e., inclusion of both labelled and unlabelled instances. Initially, the DTIP-ORNN technique performs data preparation process and also includes class labelling process, where the target interactions from the database are used to determine the final label of the unlabelled instances. Besides, drug-to-drug (D-D) and target-to-target (T-T) interactions are used for the weight initiation of the RNN based bidirectional long short term memory (BiLSTM) model which is then utilized to the prediction of DTIs. Since hyperparameters significantly affect the prediction performance of the BiLSTM technique, the Adam optimizer is used which mainly helps to improve the DTI prediction outcomes. In order to ensure the enhanced predictive outcomes of the DTIP-ORNN technique, a series of simulations are implemented on four benchmark datasets. The comparative result analysis shows the promising performance of the DTIP-ORNN method on the recent approaches.

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Keywords

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