AI has become ubiquitous across the biotech industry to help advance drug discovery. However, generic AI tools— large language model (LLM) chat interfaces—are insufficient for high-stakes scientific and regulatory environments. Those systems lack domain grounding, persistent and maintained reasoning, and the transparency required for reproducible and auditable research. This limits static LLMs to basic summarization in drug discovery, not fit for the dynamic, multistep, and decision intensive process it requires
On the other hand, Agentic AI can:
Ultimately agentic AI closely aligns with the cognitive processes and iterative nature of real-world scientific research.



