Comparison of Classical and Hybrid Quantum Models in Secondary Structure Protein Prediction

Protein structure prediction (PSP) is one of the most challenging and vital problems in modern biochemistry, with far-reaching implications for biological research and medical applications, particularly in drug design and disease treatment.This paper introduces how PSP has been tackled in recent decades, from wet lab experiments to new models like AlphaFold, as well as their computational limitations.We then present our perspective on how quantum computing can address the computational challenges in PSP, and investigate classical and quantum approaches for solving a subset of the PSP problem: a binary secondary structure classifier for protein primary sequences.We evaluate traditional feedforward neural networks against quantum neural network hybrid models using the CATH dataset.We found that the quantum-classical hybrid model outperformed the classical model on a 5-trial average, achieving 84.5% accuracy versus 79.75% with 13 input features.These results highlight the significant challenges in transitioning to tertiary structure predictions, but also the potential and need for further research in the integration of quantum methods to obtain more accurate predictions in larger, more diverse protein datasets.