Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays
Yen Sia Low, Alexander Yeugenyevich Sedykh, Ivan Rusyn and Alexander Tropsha
Affiliation: 100K Beard Hall, Campus Box 7568, University of North Carolina, Chapel Hill, NC 27599-7568, USA.
Keywords: Bioinformatics, predictive toxicology, QSAR, systems pharmacology.
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been
used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly
employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated
in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors
and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several
approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and
compare their performance across several data sets. We conclude that while no method consistently shows superior performance,
the integrative approaches rank consistently among the best yet offer enriched interpretation of models over
those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and
offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical
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