Repurposing of drugs for combined treatment of COVID 19 cytokine storm using machine learning

18 August 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Context: SARS CoV 2 induced cytokine storm is the major cause of COVID 19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. Objective: To elucidate using machine learning (ML) the set of drugs targeting a group of proteins involved in the mechanism of cytokine storm. Methods: We selected for targeting five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor Kappa B (NF 𝜅B), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3) that are involved in the SARS CoV 2 induced cytokine storm pathway. We developed ML models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID 19. Results: We identified twenty drugs that are active for four proteins and eight drugs active for five proteins. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein–ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. Conclusions: It is possible to elucidate the drugs, targeting simultaneously several proteins related to cytokine production to treat the cytokine storm in COVID 19 patients.


machine learning
multi-targeted drug discovery
screening of FDA-approved drugs


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