Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases.

Inhibitors of DNA methyltransferase (DNMT) are attractive compounds not only as potential therapeutic agents for the treatment of cancer and other diseases, but also as research tools to investigate the role of DNMTs in epigenetic events. Recent advances in high-throughput screening (HTS) for epigenetic targets and the availability of the first crystallographic structure of human DNMT1 encourage the integration of research strategies to uncover and optimize the activity of DNMT inhibitors. Herein, we present a binding model of a novel small-molecule DNMT1 inhibitor obtained by HTS, recently released in a public database. The docking model is in agreement with key interactions previously identified for established inhibitors using extensive computational studies including molecular dynamics and structure-based pharmacophore modeling. Based on the chemical structure of the novel inhibitor, a sequential computational screening of five chemical databases was performed to identify candidate compounds for testing. Similarity searching followed by molecular docking of chemical databases such as approved drugs, natural products, a DNMT-focused library, and a general screening collection, identified at least 108 molecules with promising DNMT inhibitory activity. The chemical structures of all hit compounds are disclosed to encourage the research community working on epigenetics to test experimentally the enzymatic and demethylating activity in vivo. Five candidate hits are drugs approved for other indications and represent potential starting points of a drug repurposing strategy.

CASE plots for the chemotype-based activity and selectivity analysis: a CASE study of cyclooxygenase inhibitors.

Structure-activity characterization of molecular databases plays a central role in drug discovery. However, the characterization of large databases containing structurally diverse molecules with several end-points represents a major challenge. For this purpose, the use of chemoinformatic methods plays an important role to elucidate structure-activity relationships. Herein, a general methodology, namely Chemotype Activity and Selectivity Enrichment plots, is presented. Chemotype Activity and Selectivity Enrichment plots provide graphical information concerning the activity and selectivity patterns of particular chemotypes contained in structurally diverse databases. As a case study, we analyzed a set of 658 compounds screened against cyclooxygenase-1 and cyclooxygenase-2. Chemotype Activity and Selectivity Enrichment plots analysis highlighted chemotypes enriched with active and selective molecules against cyclooxygenase-2; all this in a simple 2D graphical representation. Additionally, the most active and selective chemotypes detected in Chemotype Activity and Selectivity Enrichment plots were analyzed separately using the previously reported dual activity-difference maps. These findings indicate that Chemotype Activity and Selectivity Enrichment plots and dual activity-difference maps are complementary chemoinformatic tools to explore the structure-activity relationships of structurally diverse databases screened against two biological end-points.