Implement the Materials Genome Initiative: Machine Learning Assisted Fluorescent Probe Design for Cellular Substructure Staining

The Materials Genome Initiative (MGI) is accelerating the pace of advanced materials development by integrating high-throughput experimentation, database construction, and intelligence computation. Live-cell imaging agents, such as fluorescent dyes, are exemplary candidates for MGI applications for two reasons: i) they are essential in visualizing cellular structures and functional processes, and ii) the unclear relationship between the chemical structure of fluorescent dyes and their live-cell imaging properties severely restricts the current trial-and-error dye development. Herein, the MGI is followed to present an intelligent combinatorial methodology for predicting the staining cell ability of dyes utilizing machine learning (ML) driven by a structurally diverse combinatorial library. This study demonstrates how to high-throughput synthesize 1,536 dyes and evaluate their imaging properties to establish a feature dataset for ML. A set of high-precision ML-predictors is then successfully modeled for assisting live-cell staining and endoplasmic reticulum judgment. This approach is believed to bridge the gap between dye structure and corresponding staining behavior, and can accelerate the discovery of novel organelle-specific stains.

Automated summary

The Materials Genome Initiative (MGI) is using high-throughput experimentation, database construction, and intelligence computation to accelerate advanced materials development. In this study, an intelligent combinatorial methodology driven by machine learning is presented for predicting the staining cell ability of dyes. Through high-throughput synthesis and evaluation of 1,536 dyes, a feature dataset for machine learning is established, and high-precision ML-predictors are successfully modeled for live-cell staining and endoplasmic reticulum judgment.