Antibody binding is the basis of antibody-based therapy in cancer and autoimmunity. Antibodies are also of incredible importance in vaccine-based immune protection. Currently, antibody-drug development is slow because it is mostly based on experimental research. In this proposal, we aim to accelerate antibody research by developing novel computational approaches to antibody design.
Our approach to developing novel methods for antibody design is two-fold: we will investigate new biotechnological methods for screening antibody binding at high-throughput. We will use the newly generated data to feed into novel machine learning methods for antibody binding prediction. Finally, we will demonstrate experimentally the usability of our technology for computational antibody design.
Our proposed technology platform unlocks the possibility to design antibody binding, reducing in the long term the time and cost for therapeutics design and increasing the number of druggable targets.
TEKNOKONVERGENS-Teknologikonvergens - grensesprengende forskning og radikal innovasjon