Antibodies have become powerful preventive and therapeutic tools against infectious diseases, cancers, autoimmune disorders, and many other conditions. However, conventional antibody discovery approaches continue to face major limitations, including inefficiency, high costs, high failure rates, logistical barriers, limited scalability, and long development timelines.
We are pleased to invite Dr. Ivelin Georgiev to present his latest work on developing and validating novel wet-lab and AI-based platforms for preclinical antibody discovery.
In this webinar, Dr. Georgiev will discuss how these innovative platforms are designed to improve the efficiency, scalability, and success rate of monoclonal antibody discovery. He will also highlight how integrated experimental and computational approaches can help identify antibody candidates with challenging phenotypes that are difficult—or even impossible—to achieve using traditional discovery methods.
Key points to be discussed during the session:
- How novel wet-lab and AI-based platforms can address key bottlenecks in traditional antibody discovery
- The potential of these platforms to reduce discovery costs, timelines, and failure rates in preclinical antibody development
- How advanced discovery strategies can enable the identification of monoclonal antibody candidates with difficult-to-achieve functional phenotypes
