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[2602.13398] Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization
Summary
This article presents a novel framework for accelerating the discovery of cryoprotectant cocktails using multi-objective Bayesian optimization, enhancing cell viability and reducing experimental time.
Why It Matters
The research addresses a significant challenge in cryopreservation, where the balance between ice suppression and cell viability is critical. By improving the efficiency of cocktail discovery, this work has implications for various biological applications, potentially speeding up advancements in cell preservation technologies.
Key Takeaways
- Introduces a data-efficient framework for cryoprotectant cocktail design.
- Combines high-throughput screening with active learning for optimization.
- Demonstrates a 9.5% improvement in dominated hypervolume over naive strategies.
- Reduces experimental evaluations by 70%, saving significant time.
- Framework adaptable to different CPA libraries and objectives.
Computer Science > Machine Learning arXiv:2602.13398 (cs) [Submitted on 13 Feb 2026] Title:Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization Authors:Daniel Emerson, Nora Gaby-Biegel, Purva Joshi, Yoed Rabin, Rebecca D. Sandlin, Levent Burak Kara View a PDF of the paper titled Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization, by Daniel Emerson and 5 other authors View PDF HTML (experimental) Abstract:Designing cryoprotectant agent (CPA) cocktails for vitrification is challenging because formulations must be concentrated enough to suppress ice formation yet non-toxic enough to preserve cell viability. This tradeoff creates a large, multi-objective design space in which traditional discovery is slow, often relying on expert intuition or exhaustive experimentation. We present a data-efficient framework that accelerates CPA cocktail design by combining high-throughput screening with an active-learning loop based on multi-objective Bayesian optimization. From an initial set of measured cocktails, we train probabilistic surrogate models to predict concentration and viability and quantify uncertainty across candidate formulations. We then iteratively select the next experiments by prioritizing cocktails expected to improve the Pareto front, maximizing expected Pareto improvement under uncertainty, and update the models as new assay results are collected. Wet-lab validation shows that our approach ...
