Abstract
Background: Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway.
Method: The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models.
Results: The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset.
Conclusion: We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver “STAT3In” (https://webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.
Keywords: COVID-19; Cytokine; FDA-Approved; Inhibitors; STAT3.
【저자키워드】 COVID-19, cytokine, inhibitors, FDA-Approved, STAT3, 【초록키워드】 Cytokine storm, Cytokines, Proinflammatory response, cytokine, drug, outcome, FDA-approved drugs, inhibitors, Predictive model, COVID-19 pathogenesis, Accuracy, Data analysis, dataset, signaling pathway, Severity of disease, proinflammatory cytokines, inhibitor, predict, COVID-19 patients, AUC, Tamoxifen, lead, best, STAT3, Activation, Perindopril, FDA-approved drug, maximum, aromatic groups, machine learning classifiers, predictive models, second, identify, develop, significantly, correlated, aromatic group, build, machine learning classifier, patients with COVID-19, proliferate, the cytokine storm, 【제목키워드】 cytokine, inhibitor,