DNA methylation is associated with various diseases including psychiatric diseases, diseases of the immune system, and contributes to both the initiation and progression of various cancers. This reaction is mediated by a family of enzymes called DNA methyltransferases (DNMTs), including DNMT1. However, despite the importance of DNMT1, it has not been possible to develop drugs that allow its inhibition without potent cytotoxic effects. Thus, it is crucial to use cheminformatic tools to discover DNMT1 inhibitors that do not have cytotoxic effects.

To achieve this objective, an exhaustive search of databases was carried out. First, using the Rdkit documentation in Python 3, a code was made to establish a canonical SMILES for each compound and thus obtain its physicochemical properties. Afterwards, PUMA was used to perform an analysis of chemical space, scaffold diversity, and fingerprint similarity. Finally, 20 compounds were selected from a molecular docking analysis and different machine learning methods including Epigenetic Target Profiler. Additionally, the selected compounds are currently being evaluated for their inhibitory activity against DNMT1.