Antibodies are the fastest-growing class of therapeutic drugs, but finding ones that bind to exactly the right spot on a disease target remains slow, expensive, and often reliant on animals. This project will build computer algorithms—using fragment-based design and artificial intelligence—to design new antibodies from scratch, then test them in the lab to see if they work. Current antibody discovery methods struggle to target specific regions (epitopes) on a protein, which matters for blocking viruses or cancer cells precisely. They also give little control over whether an antibody will stay stable or dissolve properly in the body. Computational design bypasses these limits: it can tailor antibodies to hit chosen epitopes, improve their stability, and do it all without immunising animals. If the approach succeeds, it could slash the time and cost of developing antibody drugs for diseases such as cancer, autoimmune disorders, and infectious diseases. It would also give researchers a reliable tool to study how proteins interact at a molecular level. The work is primarily fundamental—establishing computational design as a competitive technology—but its direct payoff would be faster, cheaper, and more controllable therapeutic development, opening new avenues for industry investment.
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Because of their ability to bind most molecular targets tightly and specifically, antibodies are increasingly used in biomedical research, diagnostics, and medicine, where they are the fastest-growing class of therapeutics. The rapid surge in the size and quality of structural and biological databases is allowing to introduce innovative computational methods of rational antibody design. The goal of the proposed research is to develop and establish novel computational technologies of antibody discovery and optimisation, by using a multidisciplinary approach that encompasses fragment-based rational design, the development and deployment of artificial intelligence methods, in vitro experimental validation, and in vitro affinity maturation. Rational design at a computer substantially lowers the time and costs required to discover novel antibodies for a target of interest, does not exploit animals, and enables a much better control over the properties of the obtained antibodies. For example, it allows to obtain antibodies binding to specific regions of interest (epitopes) within the target (antigen), which remains a critical challenge with established technologies of antibody discovery, but is of key importance for many applications. Computational design also offers a better control over other properties essential for successful antibody development, including stability and solubility. The proposed research represents a significant step forward towards the establishment of computational design as a competitive technology for the generation of novel antibodies. Computational approaches promise to enable the reliable and inexpensive generation of drugs to combat - and tools to study - many crucial diseases. Overall, the unique opportunities offered by these approaches will enable to address new questions, accelerate discoveries by facilitating experiments, streamline therapeutic antibody development, and provide novel avenues for industry investment.
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