The fine-tuning procedure and the fitting of the surrogate model were carried out together, allowing parameters of both the pretrained pLM and the GPR model to be updated simultaneously. In this context the surrogate model, here a GPR model, served as the regression layer of the pretrained pLM. The marginal log likelihood (MLL) is use as loss function. The fine-tuning procedure was reiterated after each DMT cycle using all peptide sequences generated from prior DMT cycles.