Development of machine learning-based prediction models for chemical modulators of the retinoid X receptor (RXR) signaling pathway using public-domain bioactivity data

Authors

• S Kim

Abstract

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, public-domain bioactivity data available in PubChem (https://pubchem.ncbi.nlm.nih.gov) were used to develop machine learning-based 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 popular supervised machine learning methods (including support vector machine, random forest, neural network, k-nearest neighbors, decision tree, and naïve Bayes). The general applicability of the developed models was evaluated with external data sets from 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 bioactivity of small molecules.

Scientific Focus Area: Chemical Biology

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