Overview
A new artificial intelligence (AI) model has been developed to enhance drug and vaccine discovery by predicting the efficiency of specific mRNA sequences in protein production across various cell types. This advancement, a collaboration between The University of Texas at Austin and Sanofi, aims to streamline the development of mRNA-based therapeutics.
Key Features of the AI Model
- The model, named RiboNN, predicts how much protein cells will produce from mRNA sequences.
- It reduces the need for trial-and-error experimentation, thereby accelerating the development of mRNA therapeutics.
- RiboNN has shown to be approximately twice as accurate in predicting translation efficiency compared to previous methods.
Importance of mRNA in Medicine
Messenger RNA (mRNA) provides the instructions for protein synthesis, which is crucial for various bodily functions. The ability to create effective mRNA vaccines and drugs is vital for combating diseases such as viruses, cancers, and genetic disorders.
Research and Development Process
The research team curated a dataset from over 10,000 experiments to train the AI model. This dataset included measurements of mRNA translation efficiency in more than 140 human and mouse cell types.
Future Applications
One of the goals of RiboNN is to enable the creation of therapies targeted to specific cell types, such as liver or lung cells. This could lead to more effective treatments by optimizing mRNA sequences for increased protein production in desired locations.
Funding and Support
The project received funding from the National Institutes of Health, The Welch Foundation, and utilized the Lonestar6 supercomputer at UTβs Texas Advanced Computing Center.
Conclusion
The development of RiboNN represents a significant step forward in the field of mRNA therapeutics, potentially expediting the discovery and production of new treatments for various diseases.