Data-driven discovery of potent small molecule ice recrystallisation inhibitors.

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 si

SUBMITTER: Warren MT 

PROVIDER: S-EPMC11402961 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature