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 two reasons: i) they essential in visualizing cellular structures functional processes, ii) unclear relationship between chemical structure dyes their live-cell properties severely restricts current trial-and-error dye development. Herein, followed to present an intelligent combinatorial methodology predicting staining cell ability utilizing machine learning (ML) driven a structurally diverse library. This study demonstrates how synthesize 1,536 evaluate establish feature dataset ML. A set high-precision ML-predictors then successfully modeled assisting endoplasmic reticulum judgment. approach believed bridge gap corresponding behavior, can accelerate discovery novel organelle-specific stains.