Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, in Cell Systems, Stanford researchers are using machine learning to improve the efficacy and safety of targeted cell and gene therapies by potentially using our own proteins. 

Most human diseases occur due to the malfunctioning of proteins in our bodies, either systematically or locally. Naturally, introducing a new therapeutic protein to cure the one that is malfunctioning would be ideal. Although nearly all therapeutic protein antibodies are either fully human or engineered to look human, a similar approach has yet to make its way to other therapeutic proteins, especially those that operate in cells, such as those involved in CAR-T and CRISPR-based therapies.

The latter still runs the risk of triggering immune responses. To solve this problem, researchers at the Gao Lab have now turned to machine learning models. In this paper, we raise the question: Why not design treatments that avoid immune reactions from the start? With advances in computational tools, we’re now trying to predict which changes to a protein could trigger an immune response, and only move forward with designs that are less likely to be rejected by the body, said Xiaojing Gao, the senior author of the paper and assistant professor of chemical engineering in the School of Engineering at Stanford.

By combining three independent machine learning algorithms, the team has made significant progress toward a tool for efficiently designing proteins that avoid such immune response issues and maintain their functionality when introduced into the human body.

Source: https://news.stanford.edu/stories/2025/06/machine-learning-ai-cell-gene-therapies