A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening.

Area of Science:

• Computational chemistry and drug discovery
• Virology and infectious diseases
• Bioinformatics and computational biology

Background:

• The COVID-19 pandemic caused by SARS-CoV-2 necessitates rapid development of effective treatments due to a lack of approved vaccines and therapies.
• A multi-target drug development strategy is crucial for addressing current and future coronavirus outbreaks, potentially overcoming viral mutations.

Approach:

• Utilized VirtualFlow, an advanced in silico screening platform, for ultra-large scale virtual screening on cloud computing platforms.
• Conducted a structure-based, multi-target virtual screening campaign against 40 target sites on 17 viral and host targets, evaluating approximately 1 billion molecules.
• Investigated both active viral enzyme sites and critical protein-protein interaction interfaces to identify broad-spectrum inhibitors.

Key Points:

• The study identified top 200 in silico hits for each of the 40 target sites.
• The multi-target approach increases the probability of finding potent inhibitors and aids in developing drugs effective against viral mutations.
• Top hits binding to conserved sites are candidates for further drug development against coronaviruses.

Conclusions:

• The identified potential inhibitor candidates are made available to researchers globally to expedite drug development efforts.
• This work provides a valuable resource for the scientific community in the urgent search for anti-coronavirus therapeutics.
• The VirtualFlow platform demonstrates efficacy in large-scale virtual screening for pandemic preparedness.