Quantitative structure–activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections

Jung, Mankil; Tak, Jungae; Lee, Yongnam; Jung, Youngae

doi:10.1016/j.bmcl.2006.11.022

Cited by 31 publications

(17 citation statements)

References 35 publications

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“…Recently, Jung et al performed variable selection of tacrine derivatives against acetylcholinesterase activity using the SA-MLR, stepwise MLR, and GA-MLR methods [102]. The best equation was obtained from SA-MLR.…”

Section: Variable Selectionmentioning

confidence: 99%

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Sun

Fan

et al. 2008

J Comput Aided Mol Des

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Quantitative structure-activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug-drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.

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Section: Variable Selectionmentioning

confidence: 99%

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Sun

Fan

et al. 2008

J Comput Aided Mol Des

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“…A further study on a dataset of 80 AChE inhibitors split into training (68) and testing (12) sets (encompassing several sets of published tacrines, huprines and other compounds) using a total of 133 descriptors with stepwise multiple linear regression alone or combined with simulated annealing and genetic algorithms, resulted in test set correlations between r 2 =0.73-0.84. Several of the descriptors used in the final models were Kier shape descriptors (12). Finally, in a recent study, Manchester and Czermiński demonstrated that machine learning methods, such as Random Forest and Support Vector Regression, combined with alignment-based molecular descriptors, can efficiently model AChE inhibition activities of certain organic molecules (13).…”

Section: Introductionmentioning

confidence: 99%

“…The steric and electrostatic fields were also superimposed in the crystal structure to assist inhibitor design (11). A further study on a dataset of 80 AChE inhibitors split into training (68) and testing (12) sets (encompassing several sets of published tacrines, huprines and other compounds) using a total of 133 descriptors with stepwise multiple linear regression alone or combined with simulated annealing and genetic algorithms, resulted in test set correlations between r 2 =0.73-0.84. Several of the descriptors used in the final models were Kier shape descriptors (12).…”

Section: Introductionmentioning

confidence: 99%

Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors

Chekmarev¹,

Kholodovych²,

Kortagere

et al. 2009

Pharm Res

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“…Comparative 2D-QSAR analysis of three different classes of AChE inhibitors namely, physostigmine analogues 1,2,3,4-tetrahydroacridines and benzylamines was performed by Hansch et al [15] Akula, et al performed 3D-QSAR comparative molecular field analysis based on molecular docking on a series of bis-tacrine compounds, whereas Jung et al has used a variable selection method to derive QSAR models for tacrine derivatives [16,17] Saxena et al recently performed synthetic and 3D-QSAR analysis on diverse carbamates analogs [18,19] Recently, our group discovered dual binding site AChE inhibitors identified by pharmacophore modeling and sequential virtual screening techniques, [20] showing good pharmacokinetic profiles. Nowadays, genetic function algorithm (GFA) has gained great popularity in QSAR research.…”

Section: Introductionmentioning

confidence: 99%

Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors

Gupta

Fallarero

Vainio

et al. 2011

Molecular Informatics

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Recently discovered 42 AChE inhibitors binding at the catalytic and peripheral anionic site were identified on the basis of molecular docking approach, and its comparative quantitative structure-activity relationship (QSAR) models were developed. These structurally diverse inhibitors were obtained by our previously reported high-throughput in vitro screening technique using 384-well plate's assay based on colorimetric method of Ellman. QSAR models were developed using (i) genetic function algorithm, (ii) genetic partial least squares, (iii) support vector machine and (iv) artificial neural network techniques. The QSAR model robustness and significance was critically assessed using different cross-validation techniques on test data set. The generated QSAR models using thermodynamic, electrotopological and electronic descriptors showed that nonlinear methods are more robust than linear methods, and provide insight into the structural features of compounds that are important for AChE inhibition.

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Quantitative structure–activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections

Cited by 31 publications

References 35 publications

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors

Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors

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