Experimental validation turns a computational or theoretical claim into a question that can be tested under defined conditions. Researchers choose measurements, controls, success criteria, and replication procedures that can distinguish a genuine effect from noise, bias, contamination, or an unsuitable test.
In AI-assisted biology, validation is the boundary between a model's proposed candidate and evidence that the candidate behaves as intended. A positive result supports only what the experiment measured, while negative results and varying success rates remain valuable evidence about the method's limits.
ELI5
Experimental validation means checking an idea by performing a real test and measuring what happens. A prediction may sound convincing, but it is not the same thing as evidence from the system it is supposed to describe.
For example, software may predict that a protein will attach to a target. Researchers can make the protein and measure the interaction in a laboratory, showing which candidates work, which fail, and how often the design process succeeds.
