Radial Distribution Function descriptors for predicting affinity for vitamin D receptor

González, Maykel Pérez; Gándara, Zoila; Fall, Yagamare; Gómez, Generosa

doi:10.1016/j.ejmech.2007.10.020

Cited by 19 publications

(11 citation statements)

References 36 publications

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“…RDF050m, RDF045p (Table ), and RDF030u (Table ) are the radial distribution function (RDF) descriptors. The RDF is a group of significant descriptors for asymmetric molecules with a chiral center . They are based on the distance distribution in the molecule 19–22 .…”

Section: Resultsmentioning

confidence: 99%

“…Quantitative structure–activity relationship (QSAR) is a computational method for drug design that provides a condition to predict the activity of different molecules and to describe the chemo‐biological interactions . It also attempts to find a correlation between biological activities and molecular properties . In the present work, we applied a QSAR model on a series of calpeptin derivatives with respect to their biological activity (IC 50 ).…”

Section: Introductionmentioning

confidence: 99%

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A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Irandoost

Tahmasbpour

Harchegani

et al. 2018

J Chinese Chemical Soc

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Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R2 and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R2 test = 0.9643, RMSE test = 0.0155, R2 train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Irandoost

Tahmasbpour

Harchegani

et al. 2018

J Chinese Chemical Soc

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“…28 Additionally, the RDF descriptors can be restricted to specific atom types or distance ranges to represent specific information in a certain three-dimensional structure space (e.g., to describe the steric hindrance or the structure/activity properties of a molecule). By including characteristic atomic properties A of the atoms i and j, the RDF codes can be used in different tasks to fit the requirements of the information to be represented.…”

Section: Description Of the Models Descriptorsmentioning

confidence: 99%

A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of Ammoides atlantica

Azar

Nekoei

Riahi

et al. 2011

JSCS

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A simple, descriptive and interpretable model, based on a quantitative structure-retention relationship (QSRR), was developed using the genetic algorithm-multiple linear regression (GA-MLR) approach for the prediction of the retention indices (RI) of essential oil components. By molecular modeling, three significant descriptors related to the RI values of the essential oils were identified. A data set was selected consisting of the retention indices for 32 essential oil molecules with a range of more than 931 compounds. Then, a suitable set of the molecular descriptors was calculated and the important descriptors were selected with the aid of the genetic algorithm and multiple regression method. A model with a low prediction error and a good correlation coefficient was obtained. This model was used for the prediction of the RI values of some essential oil components which were not used in the modeling procedure.

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“…Radial distribution functions [15] were successfully employed to study the A 2A adenosine receptor agonist effect of 29 adenosine analogues [16]. A separate study focused on prediction of native receptor affinities of 38 vitamin D analogues [17] outperforming fragment-based molecular descriptors. Autocorrelation descriptors [18] were used to train ML models that predict CDK4/D inhibitory activity [19] and negative ionotropic activity of calcium entry blockers, among other applications [20].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

et al. 2013

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With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.

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Radial Distribution Function descriptors for predicting affinity for vitamin D receptor

Cited by 19 publications

References 36 publications

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of Ammoides atlantica

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

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