This shift is especially important as AI, high-throughput screening, and cryobiophysical modeling begin to enter the field. The next generation of cryoprotective agents will likely come from combining chemical design, transport physics, standardized datasets, and predictive modeling.
[6/8]
In this perspective, we argue that cryoprotectant development should be treated less like empirical formulation and more like modern drug discovery: a multiparameter optimization problem.
[3/8]
The takeaway: cryopreservation needs to move from empirical formulation toward rational, data-driven molecular design and automation. This can have major implications for biobanking, cell therapy, reproductive medicine, organ transplantation, and long-term biological preservation.
[7/8]
Instead of optimizing only for one property, such as ice inhibition, future cryoprotective agents need to be designed across multiple dimensions of:
❄️Cryoprotective efficacy
🍃Cellular and tissue safety
[4/8]
☃️ Excited to share our new paper: “Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.”
🔗 advanced.onlinelibrary.wiley.com/doi/10.1002/...
[1/8]
❄️ Cryopreservation has long relied on established cryoprotective agents such as DMSO and glycerol. These compounds work, but they also come with important trade-offs, including toxicity, delivery challenges, and limited scalability for complex tissues and organs.
[2/8]
🔀 Permeability and loading/unloading kinetics
🧊 Low-temperature solubility and viscosity
👷 Manufacturability and regulatory feasibility
❤️ Compatibility with tissue- and organ-scale preservation
[5/8]
Kudos to all authors: Dominika Wilczok, Jesus Valdes, @variniabernales.bsky.social, @aspuru.bsky.social, @elonverse.bsky.social.
[8/8]
