In peptide vaccine therapy, a peptide with high affinity for human leukocyte antigen (HLA), is important to stimulate the immune system to kill cancer cells. Several methods to predict HLA–peptide binding have been reported, but most of them rely on informatics to analyze the amino acid sequence of the peptide. Although intermolecular-interaction-based analysis is expected to improve prediction accuracy, such a method generally involves a high computational cost. Therefore, comparative binding energy (COMBINE) analysis, a 3D-quantitative structure–activity relationship method, combined with a rapidly implemented protein modeling method, was applied to solve this problem. The new method enabled quick evaluation of peptide affinity predictions with accuracy beyond a statistical method. In addition, several amino acid residues of HLA, which are known to be important for peptide binding, could be identified.