Machine learning for mixture toxicity analysis based on high-throughput printing technology

Abstract

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis. Rather than the model calculation capacity, the challenge of this process mainly comes from the lack of toxicology big-data to perform toxicity perception through the ML model. In this paper, a full strategy based a standardized high-throughput experiment was developed for Mix-tox analysis throughout the whole routine, from big-sample dataset design, model building, and training, to the toxicity prediction. Using the concentration variates as input and bio-luminescent inhibition rate as output, it turned out that a well-trained random forest algorithm was successfully applied to assess the mixtures’ toxicity effect, suggesting its value in facilitating adoption of Mix-tox analysis.