Large Language Models in Drug Development
Author Information
Author(s): Liu Xiao-huan, Lu Zhen-hua, Wang Tao, Liu Fei
Primary Institution: Jining Medical University, Jining, China
Hypothesis
The integration of large language models (LLMs) can significantly enhance drug development processes.
Conclusion
Large language models have the potential to streamline drug discovery and development, although challenges such as misinformation and verification remain.
Supporting Evidence
- LLMs can enhance communication between researchers and AI systems.
- AI applications in drug discovery can reduce costs and development time.
- ChatGPT has been used to identify potential drug leads in addiction treatment.
Takeaway
This study shows that AI tools like ChatGPT can help scientists create new medicines faster and better, but we need to be careful about the information they provide.
Methodology
The review discusses advancements in LLMs and their applications in drug development, including case studies and evaluations of their effectiveness.
Potential Biases
Potential biases in AI outputs due to training data limitations and the risk of generating false narratives.
Limitations
The study highlights the risks of misinformation and the need for rigorous verification of AI-generated information.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website