AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism
Toll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-fr...
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MDPI AG
2025-01-01
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| author | Lucindah N. Fry-Nartey Cyril Akafia Ursula S. Nkonu Spencer B. Baiden Ignatus Nunana Dorvi Kwasi Agyenkwa-Mawuli Odame Agyapong Claude Fiifi Hayford Michael D. Wilson Whelton A. Miller Samuel K. Kwofie |
| author_facet | Lucindah N. Fry-Nartey Cyril Akafia Ursula S. Nkonu Spencer B. Baiden Ignatus Nunana Dorvi Kwasi Agyenkwa-Mawuli Odame Agyapong Claude Fiifi Hayford Michael D. Wilson Whelton A. Miller Samuel K. Kwofie |
| author_sort | Lucindah N. Fry-Nartey |
| collection | DOAJ |
| description | Toll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-friendly, and easily accessible machine learning-based web application for predicting inhibitors against TLR4 by targeting the TLR4-myeloid differentiation primary response 88 (MyD88) interaction. MyD88 is a crucial adaptor protein in the TLR4-induced hyper-inflammation pathway. Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. A 10-fold cross-validation strategy was used to evaluate model performance based on metrics including accuracy, balanced accuracy, and recall. The XGBoost model demonstrated superior performance with accuracy, balanced accuracy, and recall scores of 0.994, 0.958, and 0.917, respectively, on the test. The AdaBoost and decision tree models also excelled with accuracies ranging from 0.981 to 0.992, balanced accuracies between 0.921 and 0.944, and recall scores between 0.845 and 0.891 on both training and test datasets. The XGBoost model was deployed as AICpred and was used to screen compounds that have been reported to have positive effects on mitigating the hyperinflammation-associated cytokine storm, which is a key factor in COVID-19. The models predicted Baricitinib, Ibrutinib, Nezulcitinib, MCC950, and Acalabrutinib as anti-TLR4 compounds with prediction probability above 0.90. Additionally, compounds known to inhibit TLR4, including TAK-242 (Resatorvid) and benzisothiazole derivative (M62812), were predicted as bioactive agents within the applicability domain with probabilities above 0.80. Computationally inferred compounds using AICpred can be explored as potential starting skeletons for therapeutic agents against hyperinflammation. These predictions must be consolidated with experimental screening to enhance further optimisation of the compounds. AICpred is the first of its kind targeting the inhibition of TLR4-MyD88 binding and is freely available at http://197.255.126.13:8080. |
| format | Article |
| id | doaj-art-557c63f60ec74619a8a1ed2faf7c11d7 |
| institution | Kabale University |
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| spelling | doaj-art-557c63f60ec74619a8a1ed2faf7c11d72025-01-24T13:35:13ZengMDPI AGInformation2078-24892025-01-011613410.3390/info16010034AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding MechanismLucindah N. Fry-Nartey0Cyril Akafia1Ursula S. Nkonu2Spencer B. Baiden3Ignatus Nunana Dorvi4Kwasi Agyenkwa-Mawuli5Odame Agyapong6Claude Fiifi Hayford7Michael D. Wilson8Whelton A. Miller9Samuel K. Kwofie10Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaDepartment of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra P.O. Box LG 581, GhanaDepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USADepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 77, GhanaToll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-friendly, and easily accessible machine learning-based web application for predicting inhibitors against TLR4 by targeting the TLR4-myeloid differentiation primary response 88 (MyD88) interaction. MyD88 is a crucial adaptor protein in the TLR4-induced hyper-inflammation pathway. Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. A 10-fold cross-validation strategy was used to evaluate model performance based on metrics including accuracy, balanced accuracy, and recall. The XGBoost model demonstrated superior performance with accuracy, balanced accuracy, and recall scores of 0.994, 0.958, and 0.917, respectively, on the test. The AdaBoost and decision tree models also excelled with accuracies ranging from 0.981 to 0.992, balanced accuracies between 0.921 and 0.944, and recall scores between 0.845 and 0.891 on both training and test datasets. The XGBoost model was deployed as AICpred and was used to screen compounds that have been reported to have positive effects on mitigating the hyperinflammation-associated cytokine storm, which is a key factor in COVID-19. The models predicted Baricitinib, Ibrutinib, Nezulcitinib, MCC950, and Acalabrutinib as anti-TLR4 compounds with prediction probability above 0.90. Additionally, compounds known to inhibit TLR4, including TAK-242 (Resatorvid) and benzisothiazole derivative (M62812), were predicted as bioactive agents within the applicability domain with probabilities above 0.80. Computationally inferred compounds using AICpred can be explored as potential starting skeletons for therapeutic agents against hyperinflammation. These predictions must be consolidated with experimental screening to enhance further optimisation of the compounds. AICpred is the first of its kind targeting the inhibition of TLR4-MyD88 binding and is freely available at http://197.255.126.13:8080.https://www.mdpi.com/2078-2489/16/1/34toll-like receptor 4 (TLR4)machine learninganti-inflammatoryinflammatoryinhibitorscytokine storm |
| spellingShingle | Lucindah N. Fry-Nartey Cyril Akafia Ursula S. Nkonu Spencer B. Baiden Ignatus Nunana Dorvi Kwasi Agyenkwa-Mawuli Odame Agyapong Claude Fiifi Hayford Michael D. Wilson Whelton A. Miller Samuel K. Kwofie AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism Information toll-like receptor 4 (TLR4) machine learning anti-inflammatory inflammatory inhibitors cytokine storm |
| title | AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism |
| title_full | AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism |
| title_fullStr | AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism |
| title_full_unstemmed | AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism |
| title_short | AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism |
| title_sort | aicpred machine learning based prediction of potential anti inflammatory compounds targeting tlr4 myd88 binding mechanism |
| topic | toll-like receptor 4 (TLR4) machine learning anti-inflammatory inflammatory inhibitors cytokine storm |
| url | https://www.mdpi.com/2078-2489/16/1/34 |
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