Integrating Tox21 Assay Data and Chemical Fingerprints for Neurotoxicity Prediction

To reduce reliance on in vivo testing, the U.S. Toxicology in the 21st Century (Tox21) consortium has generated quantitative high-throughput screening data for approximately 10,000 compounds (the Tox21 10K library). This study evaluates the predictive performance of Tox21 assay data and chemical structure information acting simultaneously as input descriptors to determine an independent target variable of potential compound neurotoxicity.

We constructed predictive models using five machine learning algorithms: Random Forest, Naïve Bayes, eXtreme Gradient Boosting, Support Vector Machine, and Neural Networks. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Two models were built on individual types of chemical fingerprints (ECFP4 and ToxPrint), which achieved AUC-ROC values of 0.72–0.77 and 0.67–0.71, respectively, while those using Tox21 assay data ranged from 0.66–0.73.