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Nonfouling peptides, which bind specifically to the body, are invaluable in biomedical applications such as drug delivery. However, recognizing nonfouling peptides poses a significant challenge due to the limited number of known peptides and their complex synthesis. The vast array of untested peptides makes the experimental process both costly and time-consuming. To address this, we employ protein language models (PLMs) to facilitate the recognition of nonfouling peptides. This study investigates the impact of a model ensemble on the accuracy of nonfouling peptide predictions and leverages this ensemble to test peptides from natural sources, aiming to discover more biomedically useful peptides.