Abstract
DNA methyltransferases (DNMTs) are emerging targets for the treatment of cancer and other diseases. The quinolone-based compound, SGI-1027, is a promising inhibitor of DNMT1 with a distinct mode of action and it is an attractive starting point for further research. Several experimental and computational approaches can be used to further develop novel DNMT1 inhibitors based on SGI-1027. In this work, we used a chemoinformatic-based approach to explore the potential to identify novel inhibitors in large screening collections of natural products and synthetic commercial libraries. Using the principles of similarity searching, the similarity profile to the active reference compound SGI-1027 was computed for four different screening libraries using a total of 22 two- and three- dimensional representations and two similarity metrics. The compound library with the overall highest similarity profile to the probe molecule was identified as the most promising collection for experimental testing. Individual compounds with high similarity to the reference were also selected as suitable candidates for experimental validation. During the course of this work, the 22 twoand three- dimensional representations were compared to each other and classified based on the similarity values computed with the reference compound. This classification is valuable to select structure representations for similarity searching of any other screening library. This work represents a step forward to further advance epigenetic therapies using computational approaches.
Keywords: Cancer, chemoinformatics, epigenetics, focused library, natural products, similarity searching, structure fingerprints, traditional Chinese medicine
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