Off-the-shelf foundation models have undoubtedly revolutionized industries across the board, yet when it comes to their integration into preclinical R&D, a distinct set of challenges emerge. In our newest blog post, Liran Belenzon (he/him) examines some of the limitations of current off-the-shelf LLMs, including their struggle with messy data, scientific reasoning, and personalized user needs. He also highlights why the future of AI in preclinical R&D hinges on specialized solutions tailored to the complexities of drug discovery. You can read more here: https://lnkd.in/eDjtE3pk #AI #LLMs #DrugDiscovery #PreclinicalResearch #BenchSci
Tsung Tan
3mo
Looking forward to reading. Hope curious reaction button comes back
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
3moIn your discussion, you outlined the challenges faced by off-the-shelf LLMs in preclinical R&D, emphasizing the need for specialized solutions in drug discovery. This resonates with historical patterns where specialized tools have significantly advanced scientific research. However, considering the complexity of drug development, how do you envision overcoming the trade-off between model generalization and tailoring to specific research needs, ensuring both robustness and adaptability in AI-driven preclinical studies?