The structure‑based methods have signicant advantages over sequence‑based pre‑ diction. However, they usually lack the high‑quality structural data. The rst study to relate binding afnities with a set of structures was by Horton and Lewis who used 15 ΔG values from literature as training data and used interface polar and non‑polar groups as features to obtain a linear regression coefcient of r = 0.96, and a mean absolute difference of 0.8 kcal/mol between the calculated and observed ΔG values (Horton & Lewis, 1992). Kastritis etal. (2011) benchmarked the protein–protein binding afnity data for 144 protein–protein complexes (including 19 antibody–antigen complexes) with varying biological functions and observed that the performance was poor on a validation set because of noise in the experimental data (Kastritis & Bonvin, 2011). Faster methods based on empirical functions (empirical, force‑eld‑based potentials, statistical poten‑ tials, and scoring functions used in docking) could be successful on small training sets (Audie & Scarlata, 2007; Horton & Lewis, 1992) but most of them fail to predict bind‑ ing afnity accurately (Rosato, 2010) for large datasets or discriminate between binders and non‑binders (Sacquin‑Mora etal., 2008). Vangone and Bonvin (2015) worked on relating the interfacial contacts (ICs) and noninteracting surface (NIS) residues with the experimental binding afnity and obtained a Pearson correlation of −0.73. They used a training dataset of 81 protein–protein complexes (including ten antibody–antigen com‑ plexes) and developed a method, PROtein binDIng enerGY prediction (PRODIGY), that can predict the binding afnity of protein–protein complexes from their 3D structure with a correlation of 0.73 between experimental and predicted ΔG values (Vangone & Bonvin, 2015; Xue etal., 2016). Vangone and Bonvin (2015) further analyzed 122 com‑ plexes with binding afnity data and observed that structure‑based methods such as free energy perturbation and thermodynamics integration could be very accurate, but due to their computational costs, their application is extremely limited. Apart from regression models, QSAR models were also used for relating structural descriptors with binding afnity of protein–protein complexes using structure‑based benchmark datasets com‑ piled by Kastritis etal. (2011) and Zhou etal. (2013). Using the same dataset Marillet etal. (2016) utilized 12 features which account for enthalpic and entropic changes upon binding and devised protein–protein afnity prediction models. Wang etal. used the knowledge‑based potentials and reformulated the binding afnity based on the Monte Carlo algorithm to obtain a Pearson correlation of 0.7 for the prediction (Wang, Su, etal., 2021). FoldX by Delgado etal. (2019) uses an empirical function for predicting the binding free energy between the protein–protein complexes.