2026-10-11T15:34:04Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33889522 2026-09-17T12:03:07Z portal_1197 item_type_3 month_year_09_2026

Data for Data-driven discovery of potent small molecule ice recrystallisation inhibitors Matthew T. Warren (25018339) Caroline I. Biggs (25018342) Akalabya Bissoyi (25018345) Matthew I. Gibson (25008490) Gabriele C. Sosso (25018153) Ice crystals -- Growth Crystallization Cryopreservation of organs, tissues, etc. Migrated from ePrints Controlling the formation and growth of ice is essential to successfully cryopreserve cells, tissues and biologics for research or clinical use. Current programmes to identify materials capable of modulating ice growth are guided solely by iterative changes and human intuition, with a major focus on macromolecules (proteins or polymers). This process is fundamentally constrained by a poor understanding of the mechanisms and underlying structure-activity relationships. Here, we overcome this barrier by constructing machine learning models capable of predicting the ice growth inhibition activity of small molecules. Due to current limitations in experimental throughput, we leverage ensemble models which combine state-of-the-art descriptors with domain-specific features derived from molecular simulations. When applied to virtually screen a commercial compound library, these models successfully predicted novel ice recrystallisation inhibitors that are experimentally verified to function at low millimolar concentrations. This data-driven approach will enable the discovery of new cryoprotectants to address the rapidly growing clinical and biotechnological cold-chain demands. 2024-08-23T00:00:00Z Dataset Dataset 10.82444/warw.33889522.v1 https://figshare.com/articles/dataset/Data_for_Data-driven_discovery_of_potent_small_molecule_ice_recrystallisation_inhibitors/33889522 CC BY 4.0