A key step for machine learning is peptide representation , whereby relevant structural information is converted into numerical formats for model training. Several strategies can be adopted to encode peptide information, e.g., via description of physicochemical features , onehot encoding , and/or evolutionary information . Each of these approaches captures different structural information, might be suited for different machine learning approaches , and might uniquely contribute to model performance . While public implementations are available for specific encoding methods (e.g., ), bringing them together into a single project often requires dependency alignment and possible incompatibility hurdles.