Automating biomedical literature review for rapid drug discovery: Leveraging GPT-4 to expedite pandemic response.

Objective: The rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery.

Materials And Methods: We introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat.

Machine learning for the identification of respiratory viral attachment machinery from sequences data.

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor.