Pedro Seber e Silva
Pedro Seber e Silva is a PhD candidate whose computational research uses the power of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), to solve problems related to biomedicine and bioproduction. Pedro has made contributions to the research community with powerful new ML and DL models that predict the glycosylation of monoclonal antibodies (mAbs), which are important biotherapeutics in the treatment of autoimmune conditions, infections, and cancer—and have the potential to treat many other diseases. With the support of a MathWorks Fellowship, Pedro will continue to build and refine his AI models, which could offer substantial improvements over existing models, and expand his work to study the Chinese hamster ovary cell lines used to produce mAbs, an area of great importance in long-term perfusion cultures now being developed to reduce costs and improve glycosylation control. Pedro’s work is already speeding the development of urgently needed biotherapeutics, and his work in computational modeling could help researchers in many fields—including non-experts in data science and programming—to leverage cutting-edge AI methods and tools in their work.