Neoantigens are mutated peptides arising from tumor genomic alterations, which can be recognized and attacked by the immune system, leading to antitumor immune responses. In the last decades, many immunotherapeutic strategies have been developed, which has increased the interest in neoantigens. This led to the development of computational tools that facilitate neoantigen identification and prioritization, prior to their validation using experimental approaches. This chapter aims at explaining the key steps that need to be conducted to identify potential neoantigens in silico, including an example of the most frequently used tools. This is followed by a description and comparison of the cutting-edge tools and pipelines for neoantigen prediction both for human and mouse. The last aim of this chapter is to depict the technical challenges that limit neoantigen prediction using bioinformatics, as well as the expected improvements, given the current revolution of artificial intelligence, which is implemented in most of the neoantigen-related tools. As exposed in this book chapter, we believe that advances in immunomics and computational biology will be key to implement personalized cancer immunotherapy in the clinical practice, to improve outcomes of cancer patients.