Lithiation of 2-bromo-4-(1,3-dioxolan-2-yl)-1,3-thiazole

Sinenko, V. O.; Slivchuk, Sergiy R.; Броварец, В. С.

doi:10.5267/j.ccl.2018.1.002

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…In particular, compounds bearing the thiazole ring in the molecular structure, such as isothiazolidines or isothiazolines, have antitumor, anti-allergic, anti-diabetic, anti-inflammatory, anthelmintic and anti-HIV activity. [6][7][8][9][10] The isothiazole ring is present in compounds with biological activity such as the pharmaceutical drugs ziprasidone and perospirone. 11,12 Subsequently, it should be noted, that the presence of nitro-group in the organic molecule generally stimulates additive functions of bioactivity.…”

Section: Introductionmentioning

confidence: 99%

A DFT computational study on the [3+2] cycloaddition between parent thionitrone and nitroethene

Kula¹,

Łapczuk-Krygier²

2018

10.5267/j.ccl

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Section: Introductionmentioning

confidence: 99%

A DFT computational study on the [3+2] cycloaddition between parent thionitrone and nitroethene

Kula¹,

Łapczuk-Krygier²

2018

10.5267/j.ccl

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Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

Kovalishyn

Hodyna

Sinenko

et al. 2020

CDDT

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Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

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