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

Autor: Yang J; Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America., Walker KC; Department of Biomedical Engineering, Boston University, Boston, MA, United States of America., Bekar-Cesaretli AA; Department of Chemistry, Boston University, Boston, MA, United States of America., Hao B; Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America., Bhadelia N; Chobanian & Avedisian School of Medicine and Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA, United States of America., Joseph-McCarthy D; Department of Biomedical Engineering, Boston University, Boston, MA, United States of America., Paschalidis IC; Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America; Department of Biomedical Engineering, Boston University, Boston, MA, United States of America; Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States of America. Electronic address: yannisp@bu.edu.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2024 Sep; Vol. 189, pp. 105500. Date of Electronic Publication: 2024 May 24.
DOI: 10.1016/j.ijmedinf.2024.105500
Abstrakt: 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. Our approach can be used to select PubMed articles identifying a drug target for the given virus. We tested our approach on two known pathogens: SARS-CoV-2, where the literature is vast, and Nipah, where the literature is sparse. Specifically, a panel of three experts reviewed a set of PubMed articles and labeled them as either describing a drug target for the given virus or not. The same task was given to the automated pipeline and its performance was based on whether it labeled the articles similarly to the human experts. We applied a number of prompt engineering techniques to improve the performance of ChatGPT.
Results: Our best configuration used GPT-4 by OpenAI and achieved an out-of-sample validation performance with accuracy/F1-score/sensitivity/specificity of 92.87%/88.43%/83.38%/97.82% for SARS-CoV-2 and 87.40%/73.90%/74.72%/91.36% for Nipah.
Conclusion: These results highlight the utility of ChatGPT in drug discovery and development and reveal their potential to enable rapid drug target identification during a pandemic-level health emergency.
Competing Interests: Declaration of Competing Interest The authors declare no competing interests.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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