Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico

Article Health Care Sciences & Services

Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

Rowan Howell et al.

Summary: The COVID-19 pandemic has put healthcare systems under immense pressure globally. This study presents an executable disease-stage signalling network model to predict effective repurposed drug combinations for treating COVID-19 at different stages. Through in silico screening, the study identified nine new drug combinations, with Camostat and Apilimod showing potential in suppressing viral replication in the early stage of severe disease. The study highlights the use of mechanistic modelling in rapidly evaluating personalized treatment options and provides a novel resource and expandable model system to respond to pandemic needs.

NPJ DIGITAL MEDICINE (2022)

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Article Multidisciplinary Sciences

Deep learning identifies synergistic drug combinations for treating COVID-19

Wengong Jin et al.

Summary: This study introduces a new neural network architecture for learning drug-target interactions and drug-drug synergy to aid in the discovery of drug combinations against COVID-19. By incorporating additional biological information, the model significantly outperforms previous methods in synergy prediction accuracy.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

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