Preprint Review Version 1 This version is not peer-reviewed

Computational Approaches to Identify Natural Products as Inhibitors of DNA Methyltransferases

Version 1 : Received: 6 July 2018 / Approved: 6 July 2018 / Online: 6 July 2018 (10:04:44 CEST)

How to cite: Saldívar-González, F.I.; Gómez-García, A.; Sánchez-Cruz, N.; Ruiz-Rios, J.; Pilón-Jiménez, B.A.; Medina-Franco, J.L. Computational Approaches to Identify Natural Products as Inhibitors of DNA Methyltransferases. Preprints 2018, 2018070116 (doi: 10.20944/preprints201807.0116.v1). Saldívar-González, F.I.; Gómez-García, A.; Sánchez-Cruz, N.; Ruiz-Rios, J.; Pilón-Jiménez, B.A.; Medina-Franco, J.L. Computational Approaches to Identify Natural Products as Inhibitors of DNA Methyltransferases. Preprints 2018, 2018070116 (doi: 10.20944/preprints201807.0116.v1).

Abstract

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling and other computational approaches that have been used to uncover natural products as inhibitors of DNA metiltransferases, a major family of epigenetic targets with significant potential for the treatment of cancer and several other diseases. Examples of these computational approaches include docking, similarity-based virtual screening, and pharmacophore modeling. It is also commented the chemoinformatic-based exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

Subject Areas

chemical space; chemoinformatics; data mining; databases; DNMT inhibitors; drug discovery; epi-informatics; molecular modeling; similarity searching; virtual screening

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