Year: 2024
Type: article
Abstract: Controlling the formation and growth of ice is essential to successfully cryopreserve cells, tissues and biologics. Current efforts to identify materials capable of modulating ice growth are guided by iterative changes and human intuition, with a major focus on proteins and polymers. With limited data, the discovery pipeline is constrained by a poor understanding of the mechanisms and the underlying structure-activity relationships. In this work, this barrier is overcome by constructing machine learning models capable of predicting the ice recrystallisation inhibition activity of small molecules. We generate a new dataset via experimental measurements of ice growth, then harness predictive models combining state-of-the-art descriptors with domain-specific features derived from molecular simulations. The models accurately identify potent small molecule ice recrystallisation inhibitors within a commercial compound library. Identified hits can also mitigate cellular damage during transient warming events in cryopreserved red blood cells, demonstrating how data-driven approaches can be used to discover innovative cryoprotectants and enable next-generation cryopreservation solutions for the cold chain.
Source: Nature Communications
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,790
Awards: G1198877784, Polymer Materials for Cryogenic Storage and Distribution of Biologics, G1601905568, G2593269567, Understanding ice formation in plants: finding new routes to freezing tolerance (PlantIce)., The UK High-End Computing Consortium for Biomolecular Simulation, G3401638312, G3552147583, G3835265546, Proposal for a Tier 2 Centre - HPC Midlands Plus, G4899858094, Bid for NPIF PhD studentships in AI and Data Science 2018, G6396470543, G8243181047, G881534351
OpenAlex ID: W4402552398