AI drug discovery screening for COVID-19 reveals zafirlukast as a repurposing candidate

Abstract

Aims: Over the past few years, AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process. The purpose of our study was to identify the best repurposing candidates among FDA-approved drugs, based on their predicted antiviral activity against SARS-CoV-2. Materials and methods: This article describes a drug discovery screening based on a supervised machine learning model, trained on in vitro data encoded in chemical fingerprints, representing particular molecular substructures. Predictive performance of our model has been evaluated using so-called scaffold splits offering a state-of-the-art setup for assessing model's ability to generalize to new chemical spaces, critical for drug repurposing applications. Key findings: Our study identified zafirlukast as the best repurposing candidate for COVID-19. Significance: Zafirlukast could be potent against COVID-19 both due to its predicted antiviral properties and its ability to attenuate the so called cytokine storm. Thus, these two critical mechanisms of action may be combined in one drug as a novel and promising pharmacotherapy in the current pandemic.

Keywords

• AI drug repurposing
• Machine learning
• Zafirlukast
• LTRA
• SARS-CoV-2
• COVID-19