July 24, 2024
COVID-19 Drugs

Data-Driven Collaboration at Virginia Tech Aims to Improve COVID-19 Drugs

A collaborative study conducted by researchers at Virginia Tech aims to use computer algorithms to find molecular adaptations that can improve drugs to fight COVID-19 and other diseases. The study, titled “Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios,” was recently published in the Journal of Physical Chemistry Letters.

The study focuses on generating adaptations to molecules in existing and potential medications that can enhance their ability to bind to the main protease in SARS-CoV-2, the virus that causes COVID-19. By using computer algorithms, the researchers can consider exponentially more molecular adaptations compared to traditional trial-and-error methods. The goal is to identify candidate molecule adaptations that can be created in a laboratory and tested for effectiveness.

The study presents a novel transferable data-driven framework that can accelerate the design of new small molecules and materials. The researchers explain that by changing the combination of building blocks and decorating them with functional groups, they can create molecules with desired properties.

The newly designed functionalized drug, created using this approach, showed a better half maximal effective concentration value than its parent drug and several other antivirals, including remdesivir. This measure of compound potency suggests that the functionalized drug may be more effective in treating COVID-19.

The collaborative study involved four faculty members from Virginia Tech: Sanket A. Deshmukh (associate professor of chemical engineering), Anne M. Brown (associate professor with University Libraries and the Department of Biochemistry), Andrew Lowell (assistant professor in the Department of Chemistry), and James Weger-Lucarelli (assistant professor in the Department of Biomedical Sciences and Pathobiology).

The researchers highlight the importance of cross-departmental collaboration and acknowledge the contributions of graduate students and postdoctoral students in making the study possible. The students communicated and collaborated effectively, and their involvement was crucial to the success of the study.

The functionalized molecules developed in the study were tested against live SARS-CoV-2 in a veterinary college laboratory. The results showed that the newly designed compound was more potent against the virus compared to its parent compound. This process of developing and testing functionalized molecules has the potential to go beyond the mitigation of COVID-19 and be applied to other diseases such as hepatitis E, dengue fever, and chikungunya.

The team is also exploring the application of the algorithm process in non-biological uses. The approach has versatility and can be used to functionalize and design other materials such as metal organic frameworks (MOFs), glycomaterials, and polymers.

The interdisciplinary team plans to continue their collaborations and further develop the interplay between computational prediction, chemical synthesis, and viral testing. This collaboration exemplifies the synergy between different areas of expertise and aims to develop stronger teams to tackle future challenges in drug design and development.

 

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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it