The retinoid X receptor (RXR) is a nuclear hormone receptor that functions as a transcription factor with roles in development, cell differentiation, metabolism, and cell death.  Chemicals that interfere the RXR signaling pathway may cause adverse effects on human health.  In this study, open bioactivity data available at PubChem (https://pubchem.ncbi.nlm.nih.gov) were used to develop prediction models for chemical modulators of RXR-alpha, which is a subtype of RXR that plays a role in metabolic signaling pathways, dermal cysts, cardiac development, insulin sensitization, etc.  The models were constructed from quantitative high-throughput screening (qHTS) data from the Tox21 project, using various supervised machine learning methods (including support vector machine, random forest, neural network, k-nearest neighbors, decision tree, and naïve Bayes).  The performance of the models was evaluated with an external data set containing bioactivity data submitted by ChEMBL and the NCATS Chemical Genomics Center (NCGC).  This study showcases how open data in the public domain can be used to develop prediction models for chemical toxicity.<br/>

Using open bioactivity data for developing machine-learning prediction models for chemical modulators of the retinoid X receptor (RXR) signaling pathway

256th National Meeting (2018)

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