MOATAI-VIR

MOATAI-VIR predicts disease-protein-pathway relationships for 22 clinical manifestations attributed to COVID-19’s severe adverse events. To the best of our knowledge, this is the first systematic approach to further understand the underlying etiology of COVID-19’s severe responses. More generally, MOATAI-VIR provides a clinically actionable methodology to understand the long-term consequences of viral infections.

**Preprint listed as a Hot Topic of the Day on 02/02/2021 on the Centers for Disease Control and Prevention, Public Health Genomics and Precision Health Knowledge Base webpage.**

Notice:This server is freely available to all academic and non-commercial users.
Commercial users – to use this server, or request an evaluation copy, please send an email to Dr. Jeffrey Skolnick: skolnick@gatech.edu.

Citation: Astore, C, Zhou H, Jacob J, Skolnick J. 2021. Prediction of severe adverse events, modes of action and drug treatments for COVID-19’s complications. Scientific Reports.11: 20864. PDF

Press Release: AI Tool Pairs Protein Pathways with Clinical Side Effects, Patient Comorbidities to Suggest Targeted Covid-19 Treatments

Picked up by Georgia Tech Research Horizons, Mirage News, ScienMag, MedicalXpress, Newswire, and The Medical News.

Access MOATAI-VIR

Full list of FDA-approved drugs predicted for each complication using the SARS-CoV-2 interactome.

Full list of FDA-approved drugs predicted for each complication using GWAS.

Supplementary Information

Tables included in the Supplementary Information

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Table	Description
S0	Comparison of LeMeDISCO’s J-score with the XD score, NG, SAB score and symptom similarity for correlations with comorbidity quantified by the log(RR) score, φ-score and recall.
S1	Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, pathways and the top 20 drugs for each COVID-19 clinical manifestation from the SARS-CoV-2 interactome as input results.
S2	Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, pathways and the top 20 drugs for each COVID-19 clinical manifestation from the GWAS risk genes as input results.
S3	Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, and the pathways for each uncharacterized COVID-19 manifestation from the SARS-CoV-2 interactome as input results.
S4	Hierarchically ranked comorbidities, comorbidity enriched MOA proteins, and pathways for each uncharacterized COVID-19 manifestation from the GWAS risk genes as input results.
S5	Dependence of calculated precision/enrichment factor of CoMOAdrug and CoVLS for the top 20 drugs on the cutoff of the number of known drugs associated with a disease.
S6	Interactome as input predicted neoplasm comorbidity enriched MOA proteins that are labeled as cancer associated in the COSMIC database.
S7	GWAS risk genes2 as input predicted neoplasm comorbidity enriched MOA proteins that are labeled as cancer associated in the COSMIC database.
S8	COVID-19 differentially expressed genes from Ref. 37 ranked by their adjusted p-value that are mapped to the COSMIC database.
S9	Statistical significance (p-value) of virus human interacting proteins’ overlap with the 723 COSMIC cancer drivers.

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Please send questions to Dr. Hongyi Zhou or Courtney Astore.