Scientists have developed a brand new machine learning system that would 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 discover molecules called cryoprotectants – compounds that prevent damage when freezing biological materials.
Cryoprotectants are special substances that help protect living cells and tissues from damage once they’re frozen. They work by stopping the formation of harmful ice crystals, which essentially break tissue apart once you freeze it. They also help cells maintain their structure in extreme cold.
These compounds are fundamentally necessary for preserving things like vaccines, blood samples, and reproductive cells for long-term storage or transport.
Cryopresevants could sooner or later be used to preserve organs, complex tissues, and even entire humans.
Currently, finding recent cryoprotectants is a slow, trial-and-error process. This recent ML-driven approach allows researchers to rapidly screen lots of 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 recent 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 an aminooxazole ester that outperformed many known cryoprotectants.
- Lab tests confirmed the AI’s predictions, with the brand new compound showing strong ice crystal prevention.
- The discovered molecule improved red blood cell preservation when combined with standard techniques.
The amino oxazole ester identified by the study demonstrated particularly remarkable ice recrystallization inhibition (IRI) qualities. It almost completely stopped ice crystals from growing larger throughout the freezing process.
The compound was effective even when researchers lowered its concentration. Plus, it also maintained its ice-inhibiting properties in phosphate-buffered saline (PBS), an answer that mimics the salt concentration in human bodies.
Dr. Matt Warren, the PhD student who spearheaded the project, described how the model accelerates efficiency: “After years of labour-intensive data collection within the lab, it’s incredibly exciting to now have a machine learning model that allows a data-driven approach to predicting cryoprotective activity.”
Professor Matthew Gibson from Manchester addeds, “The results of the pc model were astonishing, identifying energetic molecules I never would have chosen, even with my years of experience.”
Professor Gabriele Sosso, who led the Warwick team, explained in a blog post that, while impressive, machine learning isn’t a cure-all for all these research problems: “It’s necessary to grasp that machine learning isn’t a magic solution for each scientific problem. In this work, we used it as one tool amongst 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 spread of AI-driven studies into drug discovery and material design. Researchers have built AI models to generate interesting medicinal compounds, one in all which has been dropped at clinical trial.
DeepMind also created a model named GNoME able to routinely generating and synthesizing materials.
The recent cryoprotectant compounds discovered could have broad real-world impacts.
For instance, the researchers describe how improving cryopreservation might extend the shelf lifetime of vaccines and make it easier to move sensitive medical treatments to distant areas.
The technique could also speed up blood transfusions by reducing the time needed to process frozen blood.
While the outcomes are promising, the team cautions that more work is required to completely understand how these recent compounds function and to make sure medical safety and stability.
