QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity

Chitre, Trupti S.; Asgaonkar, Kalyani; Patil, Shital; Kumar, Sanjiv; Khedkar, Vijay M.; Garud, Dinesh R.

doi:10.1016/j.compbiolchem.2017.03.015

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…The molecular docking studies were performed with autodock Software. [19][20][21] ChemDraw Ultra 8.0 software was used to draw the structures of all the designed molecules. 2D structures were changed to 3D format.…”

Section: Methodsmentioning

confidence: 99%

“…It gives an idea about the relationship between chemical structure and biological activity. [18][19] In present study we have attempted to optimize the triazole-thiazolinone pharmacophore for antibacterial activity by two dimensional (2D) and threedimensional Quantitative structure activity relationship (3D QSAR). New compounds were designed by using Combilib tool in V-Life software and were screened by Lipinski filter.…”

mentioning

confidence: 99%

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QSAR and Molecular Docking Studies of 5-benzylideno- 2-adamantylthiazol[3,2-b][1,2,4]triazol-6(5H)ones Derivatives as Antimicrobial Activity

Chitre¹,

Parse²,

Asgaonkar³

et al. 2023

Ind. J. Pharm. Edu. Res

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To overcome the drug resistance and enhance biological activity, hybridization is the best strategy. Hybrids of 1,2,4-triazole were found to be highly active against drug-resistant pathogens and shown potential antibacterial activity. 4 Hence, 1,2,4-triazole and thiazolinone together as a hybrid have been reported to possess Staphylococcus aureus antibacterial activity. 17 Quantitative Structure-Activity relationship (QSAR) is a powerful tool for drug design. It gives an idea about the relationship between chemical structure and biological activity. [18][19] In present study we have attempted to optimize the triazole-thiazolinone pharmacophore for antibacterial activity by two dimensional (2D) and threedimensional Quantitative structure activity relationship (3D QSAR). New compounds were designed by using Combilib tool in V-Life software and were screened by Lipinski filter. The compounds having the Lipinski score of 5 were subjected to the molecular docking process by using the autodock software.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

QSAR and Molecular Docking Studies of 5-benzylideno- 2-adamantylthiazol[3,2-b][1,2,4]triazol-6(5H)ones Derivatives as Antimicrobial Activity

Chitre¹,

Parse²,

Asgaonkar³

et al. 2023

Ind. J. Pharm. Edu. Res

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“…The similarity is assessed through the distance between each data point of the training sample and the unknown sample [115]. It has often been used successfully in QSAR applications [116,117]. We have used two R implementations of the algorithm: kknn [118] and the RWeka version (IBk) [104].…”

Section: Classification Algorithmsmentioning

confidence: 99%

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu

Hovaneț

Anghel

et al. 2020

IJMS

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Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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“…In continuation of our efforts in computational studies QSAR studies were carried out to predict the desired properties of compounds for a series of thiazolidinone derivatives. 10,11 In the present study we have performed 2D QSAR with multiple linear regression (MLR) and 3D QSAR using simulated annealing k Nearest Neighbor (SA kNN) method. [12][13][14][15][16] Compounds were designed using Combilib tool and were screened with Lipinski filter to study their drug like pharmacokinetics.…”

Section: Introductionmentioning

confidence: 99%

Designing of Thiazolidin-4-one Pharmacophore using QSAR Studies for Anti-HIV Activity

Chitre¹,

Patil²,

Sujalegaonkar³

et al. 2021

IJPER

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Background and Aim:In an effort of drug development in the area of HIV, present work deals with development of 2D and 3D QSAR of thiazolidinone derivatives against HIV-RT activity as a powerful method for elucidation the relationships between structure and activity. Materials and Methods: 2D QSAR and 3D QSAR were performed using MLR and SA kNN method respectively. Models which had higher predictability were generated as indicated from their statistical parameters. Results and Discussion: Best models generated showed correlation coefficient r 2 = 0.9256 and q 2 =0.8623 for 2D QSAR and q 2 =0.8444 for 3D QSAR. The models indicated the requirement of electro topological, electrostatic and steric descriptors which would significantly contribute to HIV-RT inhibitory activity. Further a few compounds were designed using the outcome of QSAR studies.

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QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity

Cited by 13 publications

References 25 publications

QSAR and Molecular Docking Studies of 5-benzylideno- 2-adamantylthiazol[3,2-b][1,2,4]triazol-6(5H)ones Derivatives as Antimicrobial Activity

QSAR and Molecular Docking Studies of 5-benzylideno- 2-adamantylthiazol[3,2-b][1,2,4]triazol-6(5H)ones Derivatives as Antimicrobial Activity

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Designing of Thiazolidin-4-one Pharmacophore using QSAR Studies for Anti-HIV Activity

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