Computer-implemented method for determining immunogenicity of a peptide, preferably a cell surface-presented peptide, comprising the steps of: - obtaining an amino acid sequence of said peptide, wherein said peptide comprises proteinogenic and/or non-proteinogenic amino acids; - obtaining for each proteinogenic amino acid a plurality of numeric indices, each related to a physicochemical property; - obtaining a training data set comprising a positive data set and negative data set, wherein the positive set comprises (numerical) data related to a plurality of amino acid sequences of immunogenic peptides, wherein the negative set comprises (numerical) data related to a plurality of amino acid sequences of non-immunogenic peptides; - training a mathematical classification model on the training data set; - determining a likelihood of said peptide eliciting an immune response by means of the trained classification model; wherein the method comprises the steps of: - performing a principal component analysis on the numeric indices of each of the proteinogenic amino acids to obtain principal components for each analysed proteinogenic amino acid; - obtaining a feature vector for the amino acid sequences of the training data set and the amino acid sequence of said peptide, wherein a feature vector for an amino acid sequence is obtained by replacing each amino acid of said amino acid sequence by one or more corresponding principal components; wherein the classification model is trained on the feature vectors of the training data set; wherein the likelihood of said peptide being immunogenic is determined for the feature vector of said peptide; characterized in that the negative data set comprising (numerical) data related to a plurality of amino acid sequences of non-immunogenic peptides, is obtained by - obtaining amino acid sequences of peptides capable of binding to and/or being presented on a MHC; - obtaining amino acid sequences corresponding to proteome peptides; - comparing the amino acid sequences of peptides capable of binding to and/or being presented on the MHC to a proteome amino acid sequence to determine matches therebetween; the negative data set comprising the amino acid sequence of peptides capable of binding to and/or being present on the MHC that are closely related to but not identical to peptides of the proteome, said closely related amino acid sequences are having 1, 2 or 3 amino acid mismatches compared to peptides of the proteome. - obtaining an amino acid sequence of said peptide, wherein said peptide comprises proteinogenic and/or non-proteinogenic amino acids; - obtaining for each proteinogenic amino acid a plurality of numeric indices, each related to a physicochemical property; - obtaining a training data set comprising a positive data set and negative data set, wherein the positive set comprises (numerical) data related to a plurality of amino acid sequences of immunogenic peptides, wherein the negative set comprises (numerical) data related to a plurality of amino acid sequences of non-immunogenic peptides; - training a mathematical classification model on the training data set; - determining a likelihood of said peptide eliciting an immune response by means of the trained classification model; wherein the method comprises the steps of: - performing a principal component analysis on the numeric indices of each of the proteinogenic amino acids to obtain principal components for each analysed proteinogenic amino acid; - obtaining a feature vector for the amino acid sequences of the training data set and the amino acid sequence of said peptide, wherein a feature vector for an amino acid sequence is obtained by replacing each amino acid of said amino acid sequence by one or more corresponding principal components; - performing a principal component analysis on the numeric indices of each of the proteinogenic amino acids to obtain principal components for each analysed proteinogenic amino acid; - obtaining a feature vector for the amino acid sequences of the training data set and the amino acid sequence of said peptide, wherein a feature vector for an amino acid sequence is obtained by replacing each amino acid of said amino acid sequence by one or more corresponding principal components; wherein the classification model is trained on the feature vectors of the training data set; wherein the likelihood of said peptide being immunogenic is determined for the feature vector of said peptide; characterized in that the negative data set comprising (numerical) data related to a plurality of amino acid sequences of non-immunogenic peptides, is obtained by - obtaining amino acid sequences of peptides capable of binding to and/or being presented on a MHC; - obtaining amino acid sequences corresponding to proteome peptides; - comparing the amino acid sequences of peptides capable of binding to and/or being presented on the MHC to a proteome amino acid sequence to determine matches therebetween; - obtaining amino acid sequences of peptides capable of binding to and/or being presented on a MHC; - obtaining amino acid sequences corresponding to proteome peptides; - comparing the amino acid sequences of peptides capable of binding to and/or being presented on the MHC to a proteome amino acid sequence to determine matches therebetween; the negative data set comprising the amino acid sequence of peptides capable of binding to and/or being present on the MHC that are closely related to but not identical to peptides of the proteome, said closely related amino acid sequences are having 1, 2 or 3 amino acid mismatches compared to peptides of the proteome.