QSAR Studies and Design of Some Tetracyclic 1,4-Benzothiazines as Antimicrobial Agents

Mor, Satbir; Nagoria, Savita; Kumar, Ashwani; Kaushik, C. P.

doi:10.1055/s-0042-109392

Cited by 7 publications

(7 citation statements)

References 28 publications

(32 reference statements)

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“…QSAR modeling and validation: The QSAR models were developed using multiple linear regression procedure in QSARINS. The resulted QSAR models were evaluated using different parameters suggested in literature [29]. [26].…”

Section: Methodsmentioning

confidence: 99%

QSAR Studies on Microbicidal Activity of Disubstituted 1,2,3-Triazoles having Ester Linkages

Kaushik¹,

Pahwa²,

Kumar³

et al. 2018

Asian J. Chem.

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Over the past few years, continuous emergence of resistance in microorganisms along with side effects towards existing antimicrobials has become major health concern for the community. Various types of infections are increasing day by day due to these multidrug resistant bacteria and fungi. Owing to this, design and development of new structural leads has become major challenge for medicinal researchers. This justifies the attempt for synthesis of new, effective and safer antibacterial and antifungal agents with novel mode of action to combat newer microbial infections [1,2]. Triazoles correspond to class of bioactive heterocycles and have been recognized as key structural entity in several molecules of pharmaceutical importance [3]. These scaffolds reflected their role as antibacterial [4,5], antifungal [6], anti-HIV [7], anticancer [8,9], antitubercular [10,11], anticonvulsant [12], antiallergic [13], antioxidant [14], antiviral [15], antimalarial [16] agents etc., in the field of medicinal chemistry. Inspite of the pharmaceutical applications, these triazole derivatives also found to possess industrial importance as optical brighteners, dyestuffs, photostabilizers, agrochemicals and corrosion inhibitors [17,18]. In the past, various synthetic approaches have been developed for the synthesis of 1,2,3triazoles, however, Cu(I) catalyzed cycloaddition between

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Section: Methodsmentioning

confidence: 99%

QSAR Studies on Microbicidal Activity of Disubstituted 1,2,3-Triazoles having Ester Linkages

Kaushik¹,

Pahwa²,

Kumar³

et al. 2018

Asian J. Chem.

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“…53 Using these data, MLR was applied to establish antimicrobial activity QSAR models against these microorganisms. 54 After determining that WHIM parameters (Weighted Holistic Invariant Molecular descriptors) were dominant in determining the compound's activity, some of the molecules used in developing the model were modified in order to improve their activity. Another important contribution of QSAR models to antibacterial development is the improvement of existing drugs.…”

Section: Qsar In Antibacterial Compound Developmentmentioning

confidence: 99%

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Suay-García

Bueso-Bordils

Falcó

et al. 2020

WIREs Comput Mol Sci

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With the pressing issue of antibiotic resistance, there is a constant need for new antibiotics. However, the fact that traditional methods of drug discovery are expensive and time-consuming has discouraged the pharmaceutical industry, leaving the burden of discovery to research institutions. This is where quantitative structure-activity relationship (QSAR) methods become a key tool in fighting multidrug-resistant bacteria, seeing as they provide useful information for the rational design of new active molecules at a minimal cost. A variety of linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of the molecules. QSAR models have proven to be effective in rapidly providing lead compound candidates against resistant bacteria such as methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas spp., Bacillus subtilis, or Mycobacterium tuberculosis. Moreover, QSAR methods allow for a deeper analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles. The information obtained from QSAR studies makes optimizing an existing drug simpler, which is a cost-effective approach to obtain new treatments against increasingly resistant bacteria.

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“…QSAR modeling is a process to select the therapeutically active drug molecules prior to their testing in more exhaustive procedures and experimental measurements. Using this method, the biological profile of the newly designed drug candidates can be envisaged before deciding about the final synthesis experiment [1,2]. However, QSAR mod-eling must be performed in accordance with the rules framed by Organisation for Economic Cooperation and Development (OECD) [3].…”

Section: Introductionmentioning

confidence: 99%

“…; R 2 = 0.8253; Q 2 = 0.7632; R 2 − Q 2 = 0.0621; s = 0.520; MAE = 0.416; F = 61 (sub − training set) n = 14; R 2 = 0.8273; Q 2 = 0.7746; R 2 − Q 2 = 0.0527; s = 0.550; MAE = 0.420 (calibration set) n = 08; R 2 = 0.7788; Q 2 = 0.5802; R 2 − Q2 = 0.1986; s = 0.996; MAE = 0.821 (test set) n = 06; R 2 = 0.8769; s = 0.469; MAE = 0.606 (validation set) ; R 2 = 0.8660; Q 2 = 0.8268; R 2 − Q 2 = 0.0392; s = 0.430; MAE = 0.362; F = 78 (sub − training set) n = 14; R 2 = 0.8696; Q 2 = 0.8106; R 2 − Q 2 = 0.0590; s = 0.467; MAE = 0.377 (calibration set) n = 08; R 2 = 0.8793; Q 2 = 0.7590; R 2 − Q 2 = 0.1203; s = 0.507; MAE = 0.416 (test set) n = 07; R 2 = 0.7556; s = 0.569; MAE = 0.686 (validation set) ; R 2 = 0.9502; Q 2 = 0.9388; R 2 − Q 2 = 0.0114; s = 0.275; MAE = 0.209; F = 248 (sub − training set) n = 14; R 2 = 0.9376; Q 2 = 0.9219; R 2 − Q 2 = 0.0157; s = 0.444; MAE = 0.367 (calibration set) n = 07; R 2 = 0.9469; Q 2 = 0.9083; R 2 − Q 2 = 0.0386; s = 0.560; MAE = 0.450 (test set) n = 07; R 2 = 0.9003; s = 0.374; MAE = 0.338 (validation set)…”

mentioning

confidence: 99%

Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles

Kumar

2017

Drug Res (Stuttg)

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Monte Carlo method based QSAR studies for inhibitors of Mer kinase, a potential novel target for cancer treatment, has been carried out using balance of correlation technique. The data was divided into three random and dissimilar splits and hybrid optimal descriptors derived from SMILES and hydrogen filled graphs based notations were used for construction of QSAR models. The generated models have good fitting ability, robustness, generalizability and internal predictive ability. The external predictive ability has been tested using multiple criteria and described models exhibited good performance in all of these tests. The values of R, Q, R, Q, R and ∆R for the best model are 0.9502, 0.9388, 0.9469, 0.9083, 0.7534 and 0.0894 respectively. Also, the structural characteristics responsible for enhancement and reduction of activity have been extracted. Further, the agreement with the OECD rules for QSAR model has been discussed.

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QSAR Studies and Design of Some Tetracyclic 1,4-Benzothiazines as Antimicrobial Agents

Cited by 7 publications

References 28 publications

QSAR Studies on Microbicidal Activity of Disubstituted 1,2,3-Triazoles having Ester Linkages

QSAR Studies on Microbicidal Activity of Disubstituted 1,2,3-Triazoles having Ester Linkages

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles

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