Predictive Models for Human Organ Toxicity Based on In Vitro Bioactivity Data and Chemical Structure.

ABSTRACT: Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with the increasing numbers of chemicals that humans are exposed to in the environment. The purpose of this investigation was to build optimal prediction models for various human in vivo/organ-level toxicity end points (extracted from ChemIDPlus) using chemical structure and Tox21 in vitro quantitative high-throughput screening (qHTS) bioactivity assay data. Several supervised machine learning algorithms were applied to model 14 human toxicity end points pertaining to vascular, kidney, ureter and bladder, and liver organ systems. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accur

SUBMITTER: Xu T 

PROVIDER: S-EPMC10926239 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature