SBIR Phase I: Advanced Deep Learning Technologies for Designing Humanized Antibody

The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate antibody design and engineering through the development of proprietary computational approaches. Compared to conventional antibody drug development approaches that are often lengthy, costly, and inefficient, this innovation may offer a more efficient and cost-effective alternative. The proposed approach aims to create better therapeutic-grade antibodies while unlocking novel antibody design possibilities. The market opportunity addressed by the proposed technology is significant, as the global therapeutic antibody market for cancer and infectious diseases is projected to reach $235 billion by 2028. This project has the potential to transform the field of antibody discovery and provide new therapeutic options for patients. This Small Business Innovation Research (SBIR) Phase I project aims to develop an artificial intelligence (AI)-based platform that efficiently designs novel antibody drug candidates with possible lower toxicity and immunogenicity risks. The research will involve developing novel and proprietary AI based models to create best-in-class antibody therapeutics and validate them through state-of the-art in-silico experiments. To successfully complete this Phase I project the company plans to: a) develop a novel computational model to design antibody hit sequences, b) demonstrate the scalability of the proposed computational model in designing antibody hit sequences against diverse targets, c) assess biological values of the antibody hit sequences predicted by the computational model. The expected technical outcomes involve a more rapid and efficient process for designing therapeutic antibodies, resulting in lower development expenses and a quicker path to market. The AI technologies have the potential to design the most promising therapeutic antibodies to treat infectious diseases and cancer in months rather than years, reducing the time and resources needed for the pre-clinical development of therapeutic antibodies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.