MobyDigs: software for regression and classification models by genetic algorithms

Todeschini, Roberto; Consonni, Viviana; Mauri, Andrea; Pavan, Manuela

doi:10.1016/s0922-3487(03)23005-7

Cited by 140 publications

(187 citation statements)

References 5 publications

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“…In the second step, descriptors were generated using the Hyperchem-7.0 and Dragon-3.0 programs. 25,26 Dragon can calculate 1497 descriptors in 18 classes such as topological, geometrical, empirical, constitutional, charge and so on. We select more biosence and interpretable descriptors.…”

Section: Methodsmentioning

confidence: 99%

Effects of Dimaine, Diacid and Dintitro Derivatives on the Inhibition of Adenosine Deaminase; Experimental, Molecular Docking and QSAR Studies

2009

Bulletin of the Korean Chemical Society

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Effects of some diacid, diamine and dinitro aromatic compounds on the structure and activity of adenosine deaminase (ADA) were investigated by UV-Vis spectrophotometry in 50 mM phosphate buffer at pH = 7.5 and 27 o C and molecular docking studies. The results showed that all tested ligands are showing inhibition; five ligands are uncompetitive and other two ligands are mixed of competitive and noncompetetive inhibitors with majority of competitive behavior. For the later case analysis was done based on competitive inhibition. Diacids have larger size and higher inhibition constant (KI) relative to others. A logical correlation between calculated free energy of binding and experimental values was obtained for un-competitive. Experimental and calculated data showed that competitive inhibitors are distributed near the active site of enzyme and form several cluster of ranks, whereas uncompetitive inhibitors bind to the enzyme-substrate complex and distributed far from the active site. Results of structure-activity relationship showed that, larger, more hydrophobe, less spherical and more aromatic ligands have higher inhibition constants.

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Section: Methodsmentioning

confidence: 99%

Effects of Dimaine, Diacid and Dintitro Derivatives on the Inhibition of Adenosine Deaminase; Experimental, Molecular Docking and QSAR Studies

2009

Bulletin of the Korean Chemical Society

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“…In this study, a MLR approach coupled with the GA-MLR was performed using Mobydigs package versus 1.1 [39]. Accordingly, a random population of binary chromosomes (descriptor subsets) was generated.…”

Section: Qsar Model Developmentmentioning

confidence: 99%

Integrating Structure and Ligand-Based Approaches for Modelling the Histone Deacetylase Inhibition Activity of Hydroxamic Acid Derivatives

Pham-The

Le-Thi-Thu

2018

Asian J Pharm Clin Res

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Objective: Structure and ligand-based drug design approaches have be been integrated to accurately predict the inhibition activity of hydroxamic acid (HA) derivatives against the histone deacetylase-2 enzyme (HDAC2). Methods:The "active conformations" of the ligands in the binding site of the enzyme were determined by docking assays. More than 1000 0-3 dimensional molecular descriptors included in Dragon package were calculated and utilized for developing quantitative structure-activity relationship (QSAR) models through a multiple linear regression approach coupled with the genetic algorithm (GA-MLR). Results:The final model obtained showed suitable robustness and stability, with low correlation between descriptors and good predictive power. QSAR model was then used for screening bioactivity from a series of 36 novel HAs and found five candidates with very good bioactivity (half maximal inhibitory concentration<0.1 μM). Docking experiment revealed the binding mode of these compounds into the active site of HDAC2. Druglikeness and toxicity profiles of the compounds were checked through chemoinformatics tools. Conclusion:The results from this study can lead to rational design and synthesis of highly selective and potent HDAC2 inhibitors.

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“…By employing the software Mobydigs (Todeschini et al, 2009) and by maximizing the coefficient of prédiction Q 2 and minimal R 2 of S (the error). …”

Section: Descriptor Generationmentioning

confidence: 99%

Least Absolute Deviation Regression and Least Squares for Modeling Retention Indices of Set Compounds Food and Pollutants of the Environment

Mebarki¹,

Amirat²,

Mokhnach³

et al. 2017

American Journal of Applied Sciences

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Considering the importance of the statistical analysis of regression in modeling based separately on study for Quantitative structure retention indices on Carbowax 20 M (I Cw20M ) and OV-101 columns (I OV-101 ) relationships (QSRR) are determined for 114 pyrazines. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. Least Absolute Deviation regression diagnostics offers alternative dicapproaches whose main feature is the robustness. Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods. With have been applied for modeling separately retention indices of the same set of (89 pyrazines of Training and 25 of Test) eluted on Columns OV-101 and Carbowax-20M, using theoretical molecular descriptors derived from DRAGON Software and validating the results in the state approached graphically by Probability plot of the error and approached tests statistics of Anderson-Darling, in finished by the confidence interval thanks to robustness concept to check if errors distribution is really approximate.

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MobyDigs: software for regression and classification models by genetic algorithms

Cited by 140 publications

References 5 publications

Effects of Dimaine, Diacid and Dintitro Derivatives on the Inhibition of Adenosine Deaminase; Experimental, Molecular Docking and QSAR Studies

Effects of Dimaine, Diacid and Dintitro Derivatives on the Inhibition of Adenosine Deaminase; Experimental, Molecular Docking and QSAR Studies

Integrating Structure and Ligand-Based Approaches for Modelling the Histone Deacetylase Inhibition Activity of Hydroxamic Acid Derivatives

Least Absolute Deviation Regression and Least Squares for Modeling Retention Indices of Set Compounds Food and Pollutants of the Environment

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