Using theoretical descriptors in quantitative structure–property relationships: some distribution equilibria

Cronce, Donald T.; Famini, George R.; Soto, J. A. De; Wilson, Leland Y.

doi:10.1039/a802302f

Cited by 37 publications

(51 citation statements)

References 0 publications

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“…Again the separating margins determined for XlogP at the various branching points cover a large range and do not allow a general recommendation. For the molar refractivity, the margins indicating druglike compounds are somewhat higher (56,62, and 75, respectively) than in trees A, B, and E but closer to the lower limit of 70 given for the preferred range of drugs according to Ghose et al 30 The count of rotatable bonds is used to separate nondrugs from the fraction of drugs that exhibit a particularly high number of freely rotatable bonds. Within our data set we found that 15% of all drugs possess more than 12 rotatable bonds according to the more stringent definition used here.…”

Section: Resultssupporting

confidence: 53%

Gradual in Silico Filtering for Druglike Substances

Schneider

Jäckels

Andrès

et al. 2008

J. Chem. Inf. Model.

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The suitability of decision trees in comparison to support vector machines for the classification of chemical compounds into drugs and nondrugs was investigated. To account for the requirements upon screening virtual compound libraries, schemes for successive filtering steps with gradual increasing computational cost are outlined. The obtained prediction accuracy was similar between decision trees and support vector machine approaches for the applied compound data sets. By using rapidly computable variables such as druglikeness indices, XlogP, and the molar refractivity, at least 39% of the nondrugs can be filtered out, while retaining more than 83% of the actual drugs. Computationally more demanding descriptors such as specific substructure queries and quantum chemically derived variables can be postponed to subsequent classification schemes for the reduced set of compounds, whereby up to 92% of the nondrugs can be sorted out without loosing considerably more drugs. Using all available computed descriptors simultaneously in the first step did not yield significantly better results. Furthermore, the generated decision trees are used to derive guidelines for the design of druglike substances. The numerical margins found at the branching points suggest several criteria that separate drugs from nondrugs: a molecular weight higher than 230, a molar refractivity higher than 40, and the presence of one or more rings as well as one or more functional groups. Also reported are additionally required parameters to compute values for XlogP, SlogP, and the molar refractivity of boron and silicon containing compounds.

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Section: Resultssupporting

confidence: 53%

Gradual in Silico Filtering for Druglike Substances

Schneider

Jäckels

Andrès

et al. 2008

J. Chem. Inf. Model.

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“…HBDON denotes the number of hydrogen-bond donors [33] and NNITR the number of nitrogen atoms. The electrostatic hydrogen-bond acidity EHBAC is defined as the maximum positive charge on a hydrogen atom [34] while QSUMH is the sum of all atomic charges on hydrogen atoms.…”

Section: Calculation Of Descriptors and Molecular Propertiesmentioning

confidence: 99%

QSAR of Human Steroid 5α‐Reductase Inhibitors: Where are the differences between isoenzyme type 1 and 2?

Hutter¹,

Hartmann²

2004

QSAR Comb. Sci.

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Quantitative Structure Activity Relationships have been established for inhibitors of human steroid 5a-reductase including 6-azasteroids and non-steroidal compounds. From the applied descriptors, those related to the molecular geometry, electronic properties, and the electrostatic surface were derived from semi-empirical AM1 calculations. A chemical reaction as part of the inhibitory action is indicated by the presence of the ionization potential in the descriptor space. Strong similarities between the variables for the prediction of the binding affinity to the type 1 and IC 50 values for the type 2 isoform of the 5a-reductase were observed. The most pronounced differences in the linear regression QSAR equations were found for the descriptors accounting for the hydrogenbonding interaction, suggesting a different hydrogenbonding pattern in the binding pocket of both isoforms. Furthermore, the topological indices together with the surface related descriptors point towards a lower content of aromatic amino acids in the binding site of the type 2 isoenzyme. Consequences for the design of new inhibitors are discussed.

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“…Based on the LSER philosophy, a set of theoretical parameters for correlating a wide variety of properties has been developed. [13][14][15][16][17][18][19] These theoretical linear solvation energy relationship (TLSER) descriptors are summarized in Table 1. In Table 1, V m is molecular volume of solvent that can be replaced by Hildebrand solubility parameter (δ H 2 ); the values of δ H 2 are easily available in literature for most solvents.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solvent Effects on Rate Constant of [2+2] Cycloaddition Reaction of Diethyl Azodicarboxylate with Ethyl Vinyl Ether Using Artificial Neural Networks

Habibi‐Yangjeh

Nooshyar²

2005

Bulletin of the Korean Chemical Society

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Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q + ), dipole moment (µ), the Hildebrand solubility parameter (δ H 2 ) and total charges in molecule (q t ) are inputs and output of ANN is log k 2 . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k 2 of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

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Using theoretical descriptors in quantitative structure–property relationships: some distribution equilibria

Cited by 37 publications

References 0 publications

Gradual in Silico Filtering for Druglike Substances

Gradual in Silico Filtering for Druglike Substances

QSAR of Human Steroid 5α‐Reductase Inhibitors: Where are the differences between isoenzyme type 1 and 2?

Prediction of Solvent Effects on Rate Constant of [2+2] Cycloaddition Reaction of Diethyl Azodicarboxylate with Ethyl Vinyl Ether Using Artificial Neural Networks

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