A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds

Manallack, David T.; Tehan, Benjamin G.; Gancia, Emanuela; Hudson, Brian D.; Ford, Martyn G.; Livingstone, David J.; Whitley, David C.; Pitt, William R.

doi:10.1021/ci0202741

Cited by 64 publications

(47 citation statements)

References 42 publications

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“…[32] 4) Consensus modeling using ANNs have been published by Manallack and co-workers. [33] They used BCUT variables with diagonal elements consisting of charges, hydrogen bonding acceptor and donor ability, and polarizability.…”

Section: In Silico Solubility Modelsmentioning

confidence: 99%

Prediction of ADMET Properties

2006

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This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

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Section: In Silico Solubility Modelsmentioning

confidence: 99%

Prediction of ADMET Properties

2006

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“…Only a few in silico models for classification of solubility have been published, e.g., by Stahura et al [38] and Manallack et al [39]. In the present study the solubility of the compounds were determined in a turbidimetric assay at the following concentrations 0.001, 0.005, 0.01, 0.02, 0.04, 0.08, or 0.1 mg/mL.…”

Section: Discussionmentioning

confidence: 89%

In Silico Classification of Solubility using Binary k‐Nearest Neighbor and Physicochemical Descriptors

et al. 2007

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Solubility is a key property that highly affects both the absorption and screening efficiency of drug candidates. Here, we present an in silico k-nearest neighbor model applying ten physicochemical descriptors which classify solubility based on a diverse set of chemical drug candidates. Data used for modeling the solubility consisted of turbidimetric solubility values at pH 7.4 for 518 drug candidates. Data were divided into a training set and a test set of 389 and 129 compounds, respectively, which additionally were binned into two classes with respect to the solubility: insoluble class: solubility 0.02 mg/mL and soluble class: solubility > 0.02 mg/mL. Furthermore, a structural fragment analysis of soluble versus insoluble compounds was performed, and structural fragments and functional groups for which we found statistical difference in frequency between the two solubility classes were presented. Of the ten descriptors used for modeling, clog D was found to be the descriptor that separated the two solubility classes most efficiently. When the test set was classified, 84% were predicted to the right class. Validated with 12 soluble marketed drugs, the model predicted 11 of the 12 compounds to the right class. We found that the solubility model could be used to flag molecules with low solubility in an early stage of discovery projects.

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“…Particularly for classification models, a quantitative value of prediction can be used to estimate the accuracy of prediction: the nearer this value is to the closest classification label {þ1; À1}, the more reliable given prediction is supposed to be, as it was investigated in study by Manallack et al [18]. Indeed, values that are close to zero are in ''uncertainty area'' and may signal that prediction is not confident.…”

Section: Generalization For Classification Problemsmentioning

confidence: 99%

Applicability domain for in silico models to achieve accuracy of experimental measurements

Sushko

Novotarskyi

Körner

et al. 2010

Journal of Chemometrics

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The accuracy of in silico models can be inhomogeneous: models can show excellent performance on some chemical subspaces but have low accuracy on others. We show that applicability domain (AD) approaches can differentiate reliable and non-reliable predictions and identify those with experimental accuracy for both regression and classification models. For reliably predicted molecules, the predicted values can be used instead of experimental measurements. This can halve time and costs of experimental measurements. The developed classification models for AMES mutagenicity test and CYP450 inhibition, which are important drug discovery properties, are publicly available at the online chemical database and modeling environment (OCHEM) site http://qspr.eu

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A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds

Cited by 64 publications

References 42 publications

Prediction of ADMET Properties

Prediction of ADMET Properties

In Silico Classification of Solubility using Binary k‐Nearest Neighbor and Physicochemical Descriptors

Applicability domain for in silico models to achieve accuracy of experimental measurements

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