Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression

Worachartcheewan, Apilak; Nantasenamat, Chanin; Owasirikul, Wiwat; Monnor, Teerawat; Naruepantawart, Orapan; Janyapaisarn, Sayamon; Prachayasittikul, Supaluk; Prachayasittikul, Virapong

doi:10.1016/j.ejmech.2013.11.038

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…The recent study, in 2014, on quantitative structureactivity relationships (QSAR) models of a data set containing 19 compounds of 1-adamantylthiopyridine analogs with antioxidant (DPPH and SOD) activity has been reported [101]. These compounds constitute 1-adamantylthio and substituent (R) groups at positions 2-, 3-, and 4-on pyridine ring, and compounds 8-10 are in part of the data set.…”

Section: Bioactive Nicotinic Acid Derivativesmentioning

confidence: 98%

Nicotinic acid and derivatives as multifunctional pharmacophores for medical applications

Sinthupoom

Prachayasittikul

et al. 2014

Eur Food Res Technol

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Section: Bioactive Nicotinic Acid Derivativesmentioning

confidence: 98%

Nicotinic acid and derivatives as multifunctional pharmacophores for medical applications

Sinthupoom

Prachayasittikul

et al. 2014

Eur Food Res Technol

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“…QSAR can significantly reduce cost and time of drug discovery pipeline (Perkins et al 2003 ) since the method provides beneficial knowledge for rational drug design such as crucial properties or moieties required for potent activities and pharmacokinetic information (Hansch et al 2004 ). The QSAR models have been successfully constructed for understanding structure–activity relationships (SAR) of a wide range of bioactive compounds and diverse biological activities (Prachayasittikul et al 2014 ; Nantasenamat et al 2014 ; Worachartcheewan et al 2012 , 2013 , 2014a , b , c ).…”

Section: Introductionmentioning

confidence: 99%

Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification

et al. 2015

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Considerable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure–activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1–32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording RCV ranging from 0.5958 to 0.8957 and RMSECV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A–1R, 2A–2R, 7A–7R and 8A–8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1352-5) contains supplementary material, which is available to authorized users.

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“…The advantages of QSAR lie in that once the model is established, the nature of the compound can be predicted by the compound structure, and reasonable explanation can be made on the action mechanism of the drugs [13–15]. The method extends the range of rational drug screening and is helpful for finding new drugs according to the action mechanism [16–20]. …”

Section: Introductionmentioning

confidence: 99%

Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

You

Jia

et al. 2014

BioMed Research International

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Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R 2) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

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Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression

Cited by 16 publications

References 24 publications

Nicotinic acid and derivatives as multifunctional pharmacophores for medical applications

Nicotinic acid and derivatives as multifunctional pharmacophores for medical applications

Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification

Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

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