Application of Face Recognition and Deep Learning methodologies to Protein Classification.

The analysis of molecular interactions between antigens and antibodies is crucial for understanding the immunological mechanisms underlying the immune response and for developing effective therapies against various diseases. In this context, the ability to distinguish between protein interfaces that form stable complexes and those that do not is a key step in the design of therapeutic antibodies and vaccines. In recent years, deep learning models have provided advanced tools for biomedical research. This thesis focuses on the application of a face recognition methodology and a deep learning model based on a Siamese network for the analysis of these protein interactions, capable of accurately identifying pairs of interfaces that form complexes from those that do not. The model evaluates the similarity between protein interfaces images, providing an accurate classification of potentially stable molecular interactions.