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Epigenetic Target Prediction with Accurate Machine Learning Models

preprint
submitted on 05.01.2021, 14:50 and posted on 06.01.2021, 06:35 by Norberto Sánchez-Cruz, Jose L. Medina-Franco

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.

Funding

Dirección General de Cómputo y de Tecnologías de Información y Comunicación (DGTIC), project LANCAD-UNAM-DGTIC-335.

Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico, grant 282785.

Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico, scholarship number 335997.

History

Email Address of Submitting Author

medinajl@unam.mx

Institution

National Autonomous University of Mexico

Country

Mexico

ORCID For Submitting Author

0000-0003-4940-1107

Declaration of Conflict of Interest

No conflict of interest

Version Notes

Version one, not peer-reviewed.