Researchers utilize machine learning (ML) to identify structural components of computer-generated protein models. Categorizing residues as Helix, Strand, or Coil types is challenging when dealing with missing atoms or limited alpha carbon data. To overcome this, a recent study introduced a neural network-based classifier trained solely on Cα coordinates using the Keras library. By carefully selecting input features, the method achieved over 97% accuracy, surpassing existing approaches. This advancement has significant implications for understanding protein structure, aiding drug discovery, and disease treatment, highlighting ML's potential for future progress in the field.