Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

29 October 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.


machine learning
toxicity test
high-throughput experimentation
inkjet printing
enzyme reaction chips

Supplementary materials

Supplementary Materials


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