<p>Epigenetic
targets are a significant focus for drug discovery research, as demonstrated by
the eight approved epigenetic drugs for treatment of cancer and the increasing
availability of chemogenomic data related to epigenetics. This data represents
a large amount of structure-activity relationships that has not been exploited
thus far for the development of predictive models to support medicinal
chemistry efforts. Herein, we report the first large-scale study of 26318 compounds
with a quantitative measure of biological activity for 55 protein targets with
epigenetic activity. Through a systematic comparison of machine learning models
trained on molecular fingerprints of different design, we built predictive
models with high accuracy for the epigenetic target profiling of small
molecules. The models were thoroughly
validated showing mean precisions up to 0.952 for the epigenetic target
prediction task. Our results indicate that the herein reported models
have considerable potential to identify small molecules with epigenetic
activity. Therefore, our results were implemented as freely accessible and easy-to-use
web application.</p>
