The new advance, developed through an academic-industrial partnership between The University of Texas at Austin and Sanofi, helps predict how much protein cells will produce, which can minimize the need for trial-and-error experimentation, accelerating the next generation of mRNA therapeutics. Messenger RNA (mRNA) contains instructions for which proteins to make and how to make them, enabling our bodies to grow and carry out the day-to-day processes of life.

Among the most promising areas of health and medicine, the ability to develop new mRNA vaccines and drugs — able to fight viruses, cancers and genetic disorders involves the frequently challenging process of coaxing cells in a patient’s body to produce enough protein from therapeutic mRNA to effectively combat disease. The new model, called RiboNN, stands to guide the design of new mRNA-based therapeutics by illuminating what will yield the highest amount of a protein or better target specific parts of the body such as the heart or liver.

When we started this project over six years ago, there was no obvious application, said Can Cenik, an associate professor of molecular biosciences at UT Austin, who co-led the work with Vikram Agarwal, head of mRNA platform design data science in Sanofi’s mRNA Center of Excellence. We were curious whether cells coordinate which mRNAs they produce and how efficiently they are translated into proteins. That is the value of curiosity-driven research. It builds the foundation for advances like RiboNN, which only become possible much later.

Source: https://news.utexas.edu/2025/07/25/new-ai-tool-accelerates-mrna-based-treatments-for-viruses-cancers-genetic-disorders/