The discovery of new drugs and treatments can be facilitated by developing in silico tools. These new methods can guide in vitro experiments and elucidate immune mechanisms via sequence and structural analysis of biomolecules. For immunotherapy treatments, of particular interest are peptide-HLA class I (pHLA) complexes and T-cell receptors (TCRs), which play a crucial role in the immune response against viral infections and cancer. T-cell cross-reactivity, the ability of a single TCR to bind and respond to multiple pHLAs, is a significant aspect of T-cells. Predicting T-cell cross-reactivity can aid in the development of safer cancer immunotherapies and more effective viral vaccines. To this end, we first present PepSim, a novel scoring method that predicts T-cell cross-reactivity based on pHLA similarity. Our method, which is also implemented in a web-based tool, accurately distinguishes between cross-reactive and non-cross-reactive pHLAs across multiple datasets and can be utilized with any class I peptide-HLAs. Next, we leverage PepSim to identify potential vaccine targets against SARS-CoV-2 that may be cross-reactive with other SARS-CoV-2 peptides, thereby offering protection against numerous viral variants. Furthermore, we have created DINC-COVID, an ensemble docking tool that facilitates the development of SARS-CoV-2 drug therapies by taking into account ligand and receptor flexibility and generating plausible binding modes for SARS-CoV-2 proteins.