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Dr Carlos Outeiral

Carlos Outeiral

Dr Carlos Outeiral

  • Stipendiary Lecturer in Physical and Structural Biochemistry, St Peter's College
  • Eric and Wendy Schmidt AI in Science Fellow, Department of Statistics

I am an AI in Science research fellow at the Department of Statistics, working on how to use large language models to understand and engineer proteins, and funded by Schmidt Futures. My research career is quintessentially interdisciplinary: I pursued undergraduate studies in chemistry, then ventured into computational physics during my masters degree, enjoyed another unexpected turn with a doctorate in applied statistics, and have have since dedicated my postdoctoral journey to artificial intelligence.

Broadly speaking, I am interested in proteins — life's fundamental building blocks. These molecules not only form the core of some tissues (think of hair and muscles) but also regulate metabolism, facilitate nutrient transport, and serve as the linchpin of our immune response. Artificial proteins are at the vanguard of drug discovery and have marked a significant leap in the medical field, for example in treating Alzheimer's disease and various cancers. My scientific journey is interested in three broad questions: to understand how proteins work and are produced and regulated by the body; to engineer artificial proteins to make them more stable and easier to produce; and to design novel proteins that tackle some of the most interesting scientific challenges — from degrading pollutants to advancing diagnostics.


At St Peter's, most of my time is spent teaching the Physical and Molecular Biochemistry options in the first-year course, covering topics as diverse as quantum mechanics, thermodynamics, chemical kinetics and the theory of intermolecular forces, applied to understanding biochemistry. I also contribute to teaching tutorials in related topics of my expertise (enzymology, drug discovery, structural biology, data analysis, etc.) at any of the three taught years of the biochemistry course and first-year medical students.

Further to my role at St Peter's, I teach machine learning and computational biology at the Department of Statistics, and structure-based drug discovery at the Doctoral Training Centre. I have also taught courses in programming, data science and software engineering.


My research is focused on protein engineering, the subtle art of manipulating proteins to imbue them with desirable properties. This problem arises in many related scientific fields. In therapeutic discovery, once a promising drug candidate surfaces, the challenge lies in reducing its immunogenicity, eliminating aggregation, and increasing plasma half-life without compromising potency. Similarly, in synthetic biology, engineered enzymes (like PETases that rapidly degrade plastic or designed enzymes catalyzing new reactions) can be optimized by enhancing thermal stability and expressibility while maintaining or augmenting catalytic efficiency.

Where to next?