Antibodies are effective as preventive and therapeutic measures against cancers, autoimmune disorders, and other diseases. This project aims to develop novel Artificial Intelligence (AI)-based algorithms and technologies to create novel monoclonal antibody candidates for further translational efforts and clinical validation against multiple diseases. The efforts are supported by LIBRA-seq, a single-cell technology developed by the team that enables high-throughput mapping of antibody sequence to antigen specificity for a large number of antigens and B cells at a time. Initial focus is on several priority targets to include cancer and autoimmune disease therapeutic development. For each target, lead AI-generated antibody candidates will be validated in vitro and in vivo, and one antibody candidate among all targets will be selected to perform IND-enabling studies. The results will serve as proof-of-concept for the ability of AI-based approaches to design efficacious monoclonal antibodies, will confirm the generalizability of the proposed strategies, and will showcase their broad potential impact to virtually any area where monoclonal antibodies can play an important role.