Figure : Overview of the Bayesian Optimization (BO) workflow for peptide optimization. Starting from a single lead peptide, various initialization strategies were assessed, including sequence-based methods, such as alanine and homolog scanning, and structure-based predictions from known protein-peptide complexes. Within the Design-Make-Test (DMT) cycle and inner loop BO, sequence lengths were either kept fixed or allowed to vary, as well as different batch sizes were tested, to study their impact on the optimization. All the suggested peptide sequences originated from the initialization phase or from the inner loop BO are evaluated using major histocompatibility complex class I emulator(s), simulating the experimental Make and Test phases of a DMT system. Simulated experimental pIC50 values from the emulator(s) can be further degraded, by adding noise, partially replicating real lab conditions. Multiple molecular fingerprints and protein language models were tested in combination with the Gaussian process regressor model, as well as different acquisition functions. To generate new peptide sequences, a single-or multiple-objective genetic algorithm was used, guided by the scores from the acquisition functions.