EBOLApred: A machine learning-based web application for predicting cell entry inhibitors of the Ebola virus. (December 2022)

• Record Type: Journal Article
• Authors: Adams, Joseph; Agyenkwa-Mawuli, Kwasi; Agyapong, Odame; Wilson, Michael D.; Kwofie, Samuel K.
• Is Part Of: Computational biology and chemistry. Volume 101(2022)
• Publication Date: 2022-12
• Subjects: EVD Ebola virus disease -- EBOV Ebola virus -- GP glycoprotein -- VP40 Ebola virus protein 40 -- AID assay Identifier -- ML machine Learning -- RF random forest -- SVM support vector machine -- NB Naïve bayes -- kNN k-nearest neighbor -- LR logistic regression -- RFE recursive feature elimination -- TP true positives -- FP false positives -- TN true negatives -- FN false negatives -- ROC receiver operating characteristic -- AUC area under the curve -- AD applicability domain; Ebola virus protein -- Machine learning -- Inhibitors -- Support vector machine -- Random forest -- Logistic regression; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107766
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Ebola virus disease (EVD) is a highly virulent and often lethal illness that affects humans through contact with the body fluid of infected persons. Glycoprotein and matrix protein VP40 play essential roles in the virus life cycle within the host. Whilst glycoprotein mediates the entry and fusion of the virus with the host cell membrane, VP40 is also responsible for viral particle assembly and budding. This study aimed at developing machine learning models to predict small molecules as possible anti-Ebola virus compounds capable of inhibiting the activities of GP and VP40 using Ebola virus (EBOV) cell entry inhibitors from the PubChem database as training data. Predictive models were developed using five algorithms comprising random forest (RF), support vector machine (SVM), naïve Bayes (NB), k-nearest neighbor (kNN), and logistic regression (LR). The models were evaluated using a 10-fold cross-validation technique and the algorithm with the best performance was the random forest model with an accuracy of 89 %, an F1 score of 0.9, and a receiver operating characteristic curve (ROC curve) showing the area under the curve (AUC) score of 0.95. LR and SVM models also showed plausible performances with overall accuracy values of 0.84 and 0.86, respectively. The models, RF, LR, and SVM were deployed as a web server known as EBOLApred accessible via http://197.255.126.13:8000/ . Graphical Abstract: ga1 Highlights: Developed five robust machine learning algorithms to predict Ebola virus cell entry inhibitors. Random forest emerged as the most efficient for predicting anti-Ebola compounds. The novel machine learning models have been deployed as a webserver known as EBOLApred. Algorithms developed augments the search for novel therapeutic agents for Ebola virus disease.