This study in peptide characterization plays a pivotal role in advancing the accuracy and efficiency of peptide identification. Even though this research is fundamental, each step forward in understanding peptides and proteins brings us closer to practical applications in drug development, biomarker discovery, and new diagnostic tools. The incorporation of post translational modifications (PTMs) expands our research significantly. As PTMs play a crucial role in many molecular pathways, we could also gain a better understanding of protein functions and interactions and maybe in the long run improve disease diagnostics and drug development. By adopting an innovative technique, encoding at the atomic composition level, we are not only enhancing the field of proteomics but also pushing the boundaries of current state-of-the-art peptide sequence encoding techniques. Additionally, with deep learning gaining momentum, it's essential to keep up with the latest advancements in the field. Incorporating new techniques like deep learning not only enriches our research but also ensures our work remains at the forefront of scientific innovation, driving progress in peptide analysis. By laying this groundwork, we are possibly opening doors for future discoveries that could revolutionize medicine, making personalized treatments a reality and improving healthcare for people everywhere.