HLA (Human Leucocyte Antigen) class I molecules present a variable but limited repertoire of antigenic peptides for T-cell recognition. Identification of specific antigenic peptides is essential for the development of immunotherapy. High polymorphism of HLA genes and a large number of possible peptides to be evaluated, however, have made the identification by experiments costly and time-consuming. Computational methods have been proposed to address this problem. In cases where plenty number of binding affinity data of peptides are available, various QSAR and machine learning approaches efficiently evaluate the affinity of test peptides, while in the cases where just a little data are available, structure-based approaches like elaborate docking have been proposed. We have developed a software named HLABAP that is designed to predict the binding affinities for a set of peptides against a particular HLA class I allele. By the combination of homology modeling for posing instead of docking and geometry optimization of the complex structures between the HLA molecule and peptides, HLABAP well predicts the binding affinities for the peptides. The results have shown that HLABAP should be applicable to identify possible antigenic peptides against a particular allele of HLA class I prior to the experiments far efficiently than the ordinary docking methods.