Progress on the Computational Development of Epigenetic Modulators of DNA Methyltransferases 3A and 3B

Palomino-Hernández, Oscar; Jardínez-Vera, A. Christiaan; Medina‐Franco, José L.

doi:10.29356/jmcs.v61i3.353

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Interestingly, QSAR models have often been used to guide the synthesis of new molecules; thus, the descriptive approach is predominant. More recent studies focus on the use of QSAR for virtual screening (VS), and this application has been successful in finding novel chemotypes against important drug targets in diseases such as malaria, , schistosomiasis, , tuberculosis, , cancer, , and inflammation, among others . Notably, despite the exponential growth in the development of deep learning (DL) algorithms and their applications in many areas such as image and voice recognition, most of the successful QSAR case studies still use classical machine learning algorithms like multiple linear regression, partial least squares, k -nearest neighbors, support vector machines, random forest, and even shallow neural networks.…”

Section: Introductionmentioning

confidence: 99%

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

et al. 2021

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Chagas disease affects 8–11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure–activity relationship (QSAR) models for the prediction of their pIC 50 values were generated using k -nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

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Section: Introductionmentioning

confidence: 99%

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

et al. 2021

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“…Despite the fact there are two DNMT inhibitors approved for clinical use, both azacitidine and decitabine have low specificity, poor bioavailability, and instability in physiological conditions and toxicity. Therefore, it has been the interest of our [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] and several other research groups [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] to identify DNMT inhibitors with novel chemical scaffolds for further development. Inhibition of DNMTs remains a major topic of research not only because of its potential therapeutic benefits but also to understand the essential mechanisms of epigenetic events in cells.…”

Section: Introductionmentioning

confidence: 99%

Expanding the Structural Diversity of DNA Methyltransferase Inhibitors

et al. 2020

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Inhibitors of DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug discovery. They are also chemical tools to understand the biochemistry of epigenetic processes. Herein, we report five distinct inhibitors of DNMT1 characterized in enzymatic inhibition assays that did not show activity with DNMT3B. It was concluded that the dietary component theaflavin is an inhibitor of DNMT1. Two additional novel inhibitors of DNMT1 are the approved drugs glyburide and panobinostat. The DNMT1 enzymatic inhibitory activity of panobinostat, a known pan inhibitor of histone deacetylases, agrees with experimental reports of its ability to reduce DNMT1 activity in liver cancer cell lines. Molecular docking of the active compounds with DNMT1, and re-scoring with the recently developed extended connectivity interaction features approach, led to an excellent agreement between the experimental IC50 values and docking scores.

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“…However, these drugs act as covalent inhibitors and are associated with several unwanted effects. Therefore, the design and development of non-covalent DNMT inhibitors is still on the rise [4,10].…”

Section: Comparative Cheminformatic Analysis Of Inhibitors Of Dna Metmentioning

confidence: 99%