Scientists have developed a new machine learning system that could help preserve vaccines, blood, and other medical treatments.
The research, published in Nature Communications, was led by the University of Warwick and the University of Manchester.
The AI system helps identify molecules called cryoprotectants – compounds that prevent damage when freezing biological materials.
Currently, finding new cryoprotectants is a slow, trial-and-error process. This new ML-driven approach allows researchers to rapidly screen hundreds of potential molecules virtually.
Here are some key points of the study:
The team created a machine learning model trained on data from existing cryoprotectants.
This model can predict how well new molecules might work as cryoprotectants.
Researchers used the model to screen a library of about 500 amino acids.
The system identified several promising compounds, including one that outperformed many known cryoprotectants.
Lab tests confirmed the AI’s predictions, with the new compound showing strong ice crystal prevention.
The discovered molecule improved red blood cell preservation when combined with standard techniques.
Dr. Matt Warren, the PhD student who spearheaded the project, described how the model accelerates efficiency: “After years of labour-intensive data collection in the lab, it’s incredibly exciting to now have a machine learning model that enables a data-driven approach to predicting cryoprotective activity.”
One surprising outcome was the AI’s ability to identify effective molecules that experienced researchers might have overlooked.
Professor Matthew Gibson from Manchester noted, “The results of the computer model were astonishing, identifying active molecules I never would have chosen, even with my years of expertise.”
Professor Gabriele Sosso, who led the Warwick team, explained in a blog post that machine learning isn’t a cure-all for these types of research problems: “It’s important to understand that machine learning isn’t a magic solution for every scientific problem. In this work, we used it as one tool among many.”
The researchers combined the AI predictions with molecular simulations and lab experiments – a multi-pronged approach that helped validate results and refine the model.
This contributes to a range of AI-driven studies into drug discovery and material design. Researchers have built AI models to generate interesting medicinal compounds, one of which has been brought to clinical trial.
DeepMind also created a model named GNoME capable of automatically generating and synthesizing materials.
The new cryoprotectant compounds discovered could have broad real-world impacts.
For instance, the researchers describe how improving cryopreservation might extend the shelf life of vaccines and make it easier to transport sensitive medical treatments to remote areas.
The technique could also speed up blood transfusions by reducing the time needed to process frozen blood.
While the results are promising, the team cautions that more work is needed to fully understand how these new compounds function and to ensure their safety for medical use.
However, this is another intriguing compound creation study demonstrating how AI can accelerate scientific discovery in unexpected ways, potentially leading to breakthroughs in fields far beyond computer science.
