Lessons From Drug Discovery for Cryoprotective Agent Design: An AI‐Oriented Perspective

Cryopreservation is the storage of biological materials like cells, tissues, or even organs at cryogenic temperatures. This technology is a key enabler for biobanking, reproductive medicine, and cell therapy, and is positioned as a vital part of the future of transplantation. Successful cryopreservation relies on cryoprotective agents (CPAs) that protect biological structures from ice-induced damage. However, CPAs can have significant drawbacks, including toxicity, particularly at the high concentrations required for vitrification. As efforts advance toward preserving more sensitive cells, whole organs, and, ultimately, entire organisms, there is a pressing need for new CPAs with improved profiles across multiple parameters. The drug discovery discipline has long recognized that an effective compound must meet many criteria beyond potency, absorption, distribution, metabolism, elimination, and toxicity (ADME-T), and that these criteria must be balanced through multiparameter optimization. Similarly, an ideal cryoprotectant must simultaneously satisfy a broad spectrum of requirements. In this perspective, lessons from drug discovery are applied to the design of cryoprotectants. Treating cryoprotectant development as a multiparameter optimization challenge, akin to drug lead optimization, could enable systematic design of the next generation of safer and more effective CPAs.