Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Kumar, Ashwani; Chauhan, Shilpi

doi:10.1080/1062936x.2017.1293729

Cited by 32 publications

(7 citation statements)

References 59 publications

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“…DnS was the most active compound with IC 50 value of 20.4 μmol L -1 . In accordance with the previous reports and consequent predictions, it was logical to observe lipase inhibitory activity for simple aromatic acids: DnS, AsA and ScA (19,21). Most interesting perhaps was the PL inhibitory activity of the sulfonylurea drug GzD, the sulfonamide SfD and the basic drug OrP, which had not been previously reported to have such an activity.…”

Section: Lipase Inhibitory Activitysupporting

confidence: 89%

Pancreatic lipase inhibitory activity of selected pharmaceutical agents

et al. 2018

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Twenty-five structurally diverse compounds have been tested in vitro for their pancreatic lipase (PL) inhibitory activity. Despite the diversity of tested compounds, the relationship comprising structural attributes of the compounds could be established to correlate with the observed inhibitory activity. Compounds that exerted inhibitory action through surface activity were of different profile from the rest of compounds. When co-incubated with orlistat (OsT), important synergistic effects for some compounds (orphenadrine, gliclazide, cefuroxime and sulfacetamide) were revealed, while antagonistic effects were demonstrated for others (camphor sulfonic acid and dinitro salicylic acid). Docking studies for the most active molecules were performed and molecular interaction forces with the PL active site were identified. The results suggested co-binding of OsT along with the other inhibitor in the binding site in cases of synergistic effect but not in the case of antagonistic effect. These results were additionally supported by affinity capillary electrophoresis. In conclusion, synergistic lipase inhibitory activity between OsT and some other pharmaceutical compounds was demonstrated for the first time, which might help improve the pharmacological effect of OsT.

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Section: Lipase Inhibitory Activitysupporting

confidence: 89%

Pancreatic lipase inhibitory activity of selected pharmaceutical agents

et al. 2018

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“…Method 1 is a one-variable model calculated with the Monte Carlo technique [61][62][63][64] for hybrid optimal descriptors, which are calculated by simplified molecular input-line entry system (SMILES) [65], together with a molecular graph [66][67][68][69][70][71]:…”

Section: The First Weirdness Of Qspr/qsarmentioning

confidence: 99%

QSPR/QSAR: State-of-Art, Weirdness, the Future

Toropov

Toropova

2020

Molecules

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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“…The major purpose behind QSAR modeling is to achieve a model which can predict the property of the designed molecules in an appropriate way. The developed QSAR models were validated by various statistical techniques suggested in literature [31][32][33][34][35].…”

Section: Validation Of Qsar Modelmentioning

confidence: 99%

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

et al. 2020

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Fructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

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Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Cited by 32 publications

References 59 publications

Pancreatic lipase inhibitory activity of selected pharmaceutical agents

Pancreatic lipase inhibitory activity of selected pharmaceutical agents

QSPR/QSAR: State-of-Art, Weirdness, the Future

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

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