Protein-protein interactions (PPIs) are essential to understanding how proteins work together to perform most of the molecular processes that underlie life. However, capturing the structural and functional complexity of proteins, as well as their interactions, using computational representations remains an open challenge. In this study, we investigated the individual impact of contact types on binding affinity by constructing linear regression models based on interatomic contacts calculated with the COC \(\alpha \) DA tool. A curated dataset comprising 81 protein–protein complexes was employed to systematically analyze ten categories of contacts, including both specific and nonspecific interactions. Our results emphasize the significance of polar-apolar contacts (r = −0.55), evaluated at the atomic level, in reducing the binding strength between protein-protein complexes, which corroborates previous findings in the literature. Additionally, hydrogen bonds demonstrated a notable contribution to binding stability, with a Pearson correlation of r = −0.42. The results presented here are an initial step towards establishing a scoring function based solely on contact calculations.