BO is an iterative process used to suggest an optimal sets of experiments based on past observations. It relies on the main following components (Fig. ): 1) one or multiple surrogate models depending of the context (either SOO or MOO) to emulate the systems of interest (e.g. binding affinities between a peptide and one or multiple target proteins), 2) an acquisition function guiding how the peptide space should be explored during BO by taking into account the uncertainties associated to the predictions, balancing the exploitation versus the exploration, and finally 3) a sampling method (i.e. GA in this study) to generate new peptide sequences minimizing or maximizing the acquisition function. Together, they constitute what is commonly known as the inner loop of BO. This sub-task is required at the beginning of each DMT cycle to generate and suggest new peptide sequences. Additionally, if the surrogate model used is a GPR model, as it is the case in this study, a kernel function must also be defined to compute the similarity between peptide sequences. Depending on the nature of the acquisition function and the number of peptides tested at each DMT cycle, a batch selection strategy is also required to constitute the next batch of peptides to be tested. Surrogate model Compared to an actual experiment, the evaluation of the surrogate model is inexpensive, and so can be evaluated at any given point. As a surrogate model, we used the GPR model, which is a standard ML method for non-parametric regression tasks (see for a more detailed overview). So far, GPR was mainly only used as a regression model to predict peptide binding to a particular MHC allele or other protein targets .