Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories.

Motivation The recent emergence of cloud laboratories—collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality. Results We introduce a new deterministic algorithm, called P a R allel O ptimiza T i O n for C l O ud L aboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization in terms of its ability to find optimal configurations, and the number of experiments required to find the optimum. In the real-world lab, the algorithm makes progress toward the optimal setting. Data availability and implementation PROTOCOL is available as both a stand-alone Python library, and as part of a R Shiny application at https://github.com/clangmead/PROTOCOL. Data are available at the same repository. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]