The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay.
CITATION STYLE
Periwal, V., & Scaria, V. (2017). Machine learning approaches toward building predictive models for small molecule modulators of miRNA and its utility in virtual screening of molecular databases. In Methods in Molecular Biology (Vol. 1517, pp. 155–168). Humana Press Inc. https://doi.org/10.1007/978-1-4939-6563-2_11
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