The emergence of resistance mutations in the SARS-CoV-2 spike (S) protein presents a challenge for monoclonal antibody treatments like sotrovimab. Understanding the structural, dynamics, and molecular features of these mutations is essential for therapeutic advancements. However, the intricate landscape of potential mutations and critical residues conferring resistance to mAbs like sotrovimab remains elusive. This study introduces an integrated framework that combines interface protein design, machine learning, hybrid quantum mechanics/molecular mechanics (QM/MM) methodologies, all-atom and coarse-grained molecular dynamics (MD) simulations, and correlation analysis.