Focused Libraries for Epigenetic Drug Discovery: The Importance of Isosteres.
27 May 2021-Journal of Medicinal Chemistry (American Chemical Society (ACS))-Vol. 64, Iss: 11, pp 7231-7240
TL;DR: In this article, the suitability of some commercially available screening collections in the context of epigenetic drug discovery, with a particular focus on lysine post-translational modifications, was analyzed.
Abstract: Epigenetic drug discovery provides a wealth of opportunities for the discovery of new therapeutics but has been hampered by low hit rates, frequent identification of false-positives, and poor synthetic tractability. A key reason for this is that few screening collections consider the unique requirements of epigenetic targets despite significant medicinal chemistry interest. Here we analyze the suitability of some commercially available screening collections in the context of epigenetic drug discovery, with a particular focus on lysine post-translational modifications, and show that even privileged motifs found in U.S. Food and Drug Administration (FDA)-approved drugs are not present in these collections. We propose that the incorporation of epigenetic bioisosteres should become central in the design of new focused screening collections and highlight some opportunities for the development of synthetic methods which may improve the tractability of hit molecules.
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TL;DR: An overview of epigenetics drug targets is provided in this article, focusing on approaches used for initial hit identification, and subsequent role of structure-guided chemistry optimisation of initial hits to clinical candidates.
7 citations
TL;DR: An overview of epigenetics drug targets is provided in this paper , focusing on approaches used for initial hit identification, and the subsequent role of structure-guided chemistry optimisation of initial hits to clinical candidates.
7 citations
23 Aug 2021
TL;DR: In this paper, the authors review the chemical spaces explored for epigenetic drug discovery and discuss the advances in using structure-activity relationships stored in public chemogenomic databases, and discuss current efforts to chart and identify novel regions of the epigenetic relevant chemical space.
Abstract: Epigenetic drug discovery is a promising avenue to find therapeutic agents for treating several diseases and developing novel chemical probes for research. In order to identify hit and lead compounds, the chemical space has been explored and screened, generating valuable bioactivity information that can be used for multiple purposes such as prediction of the activity of existing chemicals, e.g., small molecules, guiding the design or optimization of compounds, and expanding the epigenetic relevant chemical space. Herein, we review the chemical spaces explored for epigenetic drug discovery and discuss the advances in using structure-activity relationships stored in public chemogenomic databases. We also review current efforts to chart and identify novel regions of the epigenetic relevant chemical space. In particular, we discuss the development and accessibility of two significant types of compound libraries focused on epigenetic targets: commercially available libraries for screening and targeted chemical libraries using de novo design. In this mini-review, we emphasize inhibitors of DNA methyltransferases.
4 citations
TL;DR: Isoflavone derivatives were prepared from benzoylbenzofuran precursors, and the synthesized compounds were analyzed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy, as well as high-resolution mass spectrometry (HRMS) to confirm their structures as discussed by the authors .
3 citations
TL;DR: In this article , effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions, and the proposed strategy turned out to reduce the false discovery rate successfully.
Abstract: Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.
2 citations
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Abstract: The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates
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