QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

Murcia-Soler, Miguel; Pérez-Giménez, Facundo; Nalda-Molina, Ricardo; Mt, Salabert-Salvador; Fj, García-March; Ra, Cercós-del-Pozo; Garrigues, Teresa María

doi:10.1021/ci010281+

Cited by 16 publications

(26 citation statements)

References 20 publications

(21 reference statements)

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“…We used the same atom properties to calculate the global topological charge index (GTCI) [50] and the eigenvalue descriptor burden modified eigenvalues (BCUT) [1]. Furthermore we calculated the Moreau -Broto autocorrelation ACðdÞ and the Radial Distribution Function (RDF) [51].…”

Section: Parameters -Vector-based Codingmentioning

confidence: 99%

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

et al. 2006

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We present a classification method, which is based on a coordinate-free chemical space. Thus, it does not depend on descriptor values commonly used by coordinate-based chemical space methods. In our method the molecular similarity of chemical structures is evaluated by a generalized maximum common graph isomorphism, which supports the usage of numerical physicochemical atom property labels in addition to discrete-atomtype labels. The Maximum Common Substructure (MCS) algorithm applies the Highest Scoring Common Substructure (HSCS) ranking of Sheridan and co-workers, which penalizes discontinuous fragments. For all compared classification algorithms used in this work we analyze their usefulness based on two objectives. First, we are interested in highly accurate and general hypotheses and second, the interpretation ability is highly important to increase our structural knowledge for the ADME data sets and the activity data set investigated in this work.

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Section: Parameters -Vector-based Codingmentioning

confidence: 99%

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

et al. 2006

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“…Murcia-Soler et al [4] used molecular connectivities (a type of topological index) to model the anti-hyperglycaemic potency of 13 sulphonylurea drugs. Other workers have modelled glitazones [5][6][7][8], spirohydantoins [9,10], PTP1B inhibitors [11] and flavonoids [12].…”

Section: Introductionmentioning

confidence: 99%

“…Other workers have modelled glitazones [5][6][7][8], spirohydantoins [9,10], PTP1B inhibitors [11] and flavonoids [12]. Murcia-Soler et al [4] also used molecular connectivities to model the acute toxicity (LD 50 ) *Corresponding author. Email: j.c.dearden@ljmu.ac.uk and plasma protein-binding (%PPB) of the sulphonylurea drugs.…”

Section: Introductionmentioning

confidence: 99%

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QSAR study of some anti-hyperglycaemic sulphonylurea drugs

Dearden

Hewitt

Rowe

2015

SAR and QSAR in Environmental Research

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Sulphonylureas are widely used anti-hyperglycaemic drugs for the treatment of type 2 diabetes. The only published quantitative structure-activity relationship (QSAR) models for sulphonylurea drugs have been found to be questionable, for a number of reasons. We have re-analysed the human anti-hyperglycaemic potencies, acute mouse intraperitoneal toxicities (LD50) and plasma protein-binding abilities of the 15 drugs using multiple linear regression and obtained good QSAR models for each endpoint. The obtained QSARs all comply well with the Organisation for Economic Co-operation and Development (OECD) Guidelines for the Validation of (Q)SARs. We could not carry out external validation of our models for acute toxicity and plasma protein-binding because of the very small datasets available.

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“…Woo et al (2002) reported mechanism-based structure-activity relationships analysis in carcinogenic for drinking water DBPs. The QSAR analysis of hypoglycemic agents also has been conducted by using the topological indices (Murcia-Soler et al, 2001). In regards to QSAR model process and model validation, linear QSAR regression models have been studied for the prediction of bio-concentration factors by physicochemical properties, and structural theoretical molecular descriptors are studied by Papa et al (2007).…”

Section: Quantitative Structure-activity Relationship (Qsar)mentioning

confidence: 99%

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Wang¹

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Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO ) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as E LUMO is using QSAR analysis. In this study, number of chlorine (N Cl ) and number of carbon (N C ) as well as energy of the highest occupied molecular orbital (E HOMO ) are used as molecular descriptors.vii There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro-or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature.The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) E LUMO are shown to correlate highly with the N Cl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from N Cl for all DBP classes. ix IntroductionDuring water treatment processes, the disinfection is commonly used to destroy pathogenic organisms and prevent the outbreak of waterborne infectious diseases.Although the benefits of water disinfection are well documented, there is an undesirable side effect of producing various Disinfection By-Products (DBPs) when disinfectants such as chlorine react with natural inorganic and organic matters in the water. Quantitative Structure-Activity Relationship (QSAR)QSAR analysis is a promising tool based on the assumption that t...

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QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

Cited by 16 publications

References 20 publications

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

QSAR study of some anti-hyperglycaemic sulphonylurea drugs

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

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