A biological hypothesis is a testable explanation for how a drug, protein target, pathway, or other factor may affect a living system. It is not a conclusion; it is a claim that can be checked with experiments, such as cell assays, animal studies, or clinical measurements.
In AI-assisted drug discovery, the hypothesis is the core idea the model helps prioritize. If the underlying biology is weak or incomplete, the system may generate convincing-looking predictions that are still wrong. That matters because fast, high-confidence outputs can amplify bad assumptions. Strong workflows use the hypothesis as a gate: model output suggests candidates, wet-lab tests confirm or reject them, and external data checks whether the idea generalizes. The goal is not to produce more guesses, but to reject weak ones early.