Bat-inspired Optimizer for Prediction of Anti-Viral Cure Drug of SARS-CoV-2 based on Recurrent Neural Network

Abstract

COVID-19 is a large family of viruses that causes diseases ranging from the common cold to more severe diseases such as SARS-CoV. There are currently several attempts to create an anti-viral drug to combat the virus. The antiviral medicines could be promising treatment choices for COVID-19. Therefore, a fast strategy for drugs application that can be utilized to the patient immediately is necessary. In this context, deep learning-based architectures can be considered for predicting drug-target interactions accurately. This is due to a large amount of complicated knowledge, such as hydrophobic interactions, ionic interactions, and bonding with hydrogen. In this paper, Recurrent Neural Network (RNN) is used to build drug-target interaction prediction model to predict drug-target interactions. Bat Algorithm (BA) is used in this paper to optimize the model parameters of RNN (RNN-BA) and then using the corona virus as a target. The drug with the best binding affinity will be a potential cure for the virus. The proposed model consists of different four phases; data preparation phase, hyper-parameters optimizing phase, learning phase and fine-tuning for specific ligand subsets. The used dataset in this paper to train and evaluate the proposed model is selected from a total of 677,044 SMILES. The experimental results of the proposed model showed high level of performance in comparison with the related approaches.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

“…The process of diagnosing and detection was performed using techniques of segmentation using fuzzy c-means, and Discrete Wavelet Transform (DWT) did the classification process. The Bat optimizer performed the optimization of the extracted features [ 70 ]. developed a method to predict drug-target interactions using the Recurrent Neural Network model.…”

“…37% F-mean—97.52% G-mean—97.38% Accuracy—97.43% [ 87 ] Grey wolf optimization Feature selection and identification of Lung Diseases Accuracy The proposed performed well with the classification and consumed less computation cost as well as computed high accuracy The work should include more datasets as well as deep learning techniques to optimize the performance With k-NN—99.4% With random forest– 99.2% With SVM(Linear)– 99.0% With decision tree—98.4% [ 78 ] Bat algorithm Diabetes mellitus detection Accuracy—98.65% The proposed algorithm showed their superiority in terms of their performance The algorithm should be tested on other chronic diseases . [ 70 ] Recurrent neural network using Bat optimizer Anti viral cure drug of SARS CoV-2 Accuracy—96.08% The proposed model showed the best performance of prediction The model should be incorporated in clinical settings [ 88 ] Grey wolf optimization Brain tumor Accuracy—94.1% The performance of the proposed model was better than the existing ones as mentioned in their paper The model was trained with smaller size of dataset Sensitivity- 88.9% Specificity- 100% Precision- 100% [ 89 ] Grey wolf optimization using fuzzy logic Diabetes prediction Accuracy—81% The proposed algorithm showed a great potential in long term outcomes The system worked on few parameters to predict the diab...…”