Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.

Area of Science:

• Computational drug discovery
• Virology
• Pharmacology

Background:

• The SARS-CoV-2 pandemic necessitated rapid development of treatments.
• Existing knowledge of SARS-CoV-2 was limited at the pandemic's onset.
• Drug repurposing and novel therapeutic discovery are crucial for emerging infectious diseases.

Purpose of the Study:

• To identify small molecule inhibitors for treating COVID-19 using the CANDO platform.
• To leverage computational methods for rapid drug candidate prediction against SARS-CoV-2.
• To validate the efficacy of predicted drug candidates through experimental and clinical studies.

Main Methods:

• Utilized the CANDO platform for multiscale therapeutic discovery and repurposing.
• Optimized drug candidate prediction pipelines using high-throughput screening data from related coronaviruses.
• Employed viral protein inhibition and proteomic interaction similarity pipelines for candidate ranking.
• Applied open-source cando.py software for compound-protein interaction scoring and analysis.

Main Results:

• Generated ranked lists of drug candidates at the early stages of the COVID-19 pandemic.
• 51 out of 276 predicted drug candidates demonstrated anti-SARS-CoV-2 activity in published studies.
• The platform successfully identified compounds with potential therapeutic benefits against SARS-CoV-2.
• The study highlights the effectiveness of computational approaches in responding to emergent pathogens.

Conclusions:

• The CANDO platform demonstrates rapid response capability for emergent viral threats.
• Treating compounds in a multitarget context provides a more accurate prediction of their biological behavior.
• Computational drug discovery platforms are vital tools for pandemic preparedness and response.
• The findings support the use of computational methods for accelerating the development of antiviral therapies.