GANANA is a Europe-India partnership for Scientific High-Performance Computing aiming to leverage the power of HPC to progress on global challenges within life sciences, geophysical hazards, and climate and weather. Withing GANANA, we contribute to the life science pillar through research on accelerating the GROMACS molecular dynamics software and developing a framework for computational AI-driven immunogenic peptide design. Targeted immunotherapy that uses short peptides to elicit an immune response to eliminate infected or cancerous cells, offers a promising therapeutic approach for immunologic diseases. Here, an immune response is initiated when the Major histocompatibility complex (MHC) and T-cell receptor (TCR) recognize and bind to a peptide fragment from a foreign protein - either originating from an infectious agent or a cancer cell - leading to activation of T-cells that attack the defect cell. However, identifying peptide epitopes and structural properties of the receptors that will actually lead to an immunogenic response is challenging. The purpose of our project is therefore to develop a large-scale computational workflow involving multiple computational methods such as deep-learning of receptor and peptide sequences, structure prediction, molecular docking, and molecular dynamics simulations, including advanced enhanced sampling techniques such as the Average Weight Histogram method, to identify MHC/peptide/TCR combinations with potential immunogenic properties. Within the GANANA collaboration, we are primarily focusing on developing a molecular dynamics module capable of ranking the physical viability of the AI-generated peptide/receptor complex designs and to compute useful immunogenic properties, such as binding affinity, from the simulations, which can then be used for further training of the neural network for sequence prediction. Although identifying receptor/peptide combinations capable of eliciting T-cell immunogenic response is a difficult problem, a successfully built workflow could ultimately serve as a tool for epitope-based vaccine development targeting both infectious diseases and certain types of cancer. Here, the majority of the requested resources will be invested. We are also working on accelerating and enhancing molecular dynamic simulations through algorithmic improvements to the molecular dynamics software, GROMACS. These include adding fixed precision coordinates to reduce overhead in memory and computation and modernizing communication algorithms on new hardware architectures. This part of the project would greatly benefit from continuous access to computational resources to assess the scaling and performance of the new implementations via large-scale benchmarking on an ongoing basis.