Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library. (30th May 2022) - Gui, Bingxin; Wang, Chen; Xu, Xiaotian; Li, Chao; Zhao, Yuanhui; Su, Limin
Abstract: Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment. Highlights: Simple algorithms are applied to identify chemicals causing genotoxicity. Binary logistic regression and RF classifier models have satisfactory results. The parameters log S and acid p K a play dominant roles in the prediction. Models can be used to predict genotoxicity across understudied chemicals.