From Narcosis to Hyperspace: The History of QSAR

Kubinyi, Hugo

doi:10.1002/1521-3838(200210)21:4<348::aid-qsar348>3.0.co;2-d

Cited by 88 publications

(48 citation statements)

References 36 publications

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“…The higher Q 2 EXT value and the smaller RMSE value calculated for the test set of the logP-based model (6) highlight its better performance in predictions, at least for the selected test compounds, than the logP-free one (7). However, it must be pointed out that both models have a relative internal stability, as demonstrated by the quite low Q 2 BOOT values, and this instability is higher in the logP based model.…”

Section: Resultsmentioning

confidence: 93%

Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)

Papa

Villa

Gramatica

2005

J. Chem. Inf. Model.

162

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The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.

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Section: Resultsmentioning

confidence: 93%

Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)

Papa

Villa

Gramatica

2005

J. Chem. Inf. Model.

162

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“…A search of the literature quickly highlights the need for such studies [1][2][3][4][5][6][7], given the inherent limitations of long standing techniques such as cross-validation. The same cannot be said for most other areas of computational chemistry, despite the fact that many of the same problems permeate the field.…”

Section: Introductionmentioning

confidence: 99%

Measuring CAMD technique performance: A virtual screening case study in the design of validation experiments

Good

Hermsmeier

Hindle³

2004

J Comput Aided Mol Des

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The dynamic nature and comparatively young age of computational chemistry is such that novel algorithms continue to be developed at a rapid pace. Such efforts are often wrought at the expense of extensive experimental validations of said techniques, preventing a deeper understanding of their potential utility and limitations. Here we address this issue for ligand-based virtual screening descriptors through design of validation experiments that better reflect the aims of real world application. Applying the newly defined chemotype enrichment approach, a variety of two- and three-dimensional (2D/3D) similarity descriptors have been compared extensively across data sets from four diverse target types. The inhibitors within said data sets contain molecules exhibiting a wide array of substructure functionality, size and flexibility, permitting descriptor comparison in myriad settings. Relative descriptor performance under these conditions is examined, including results obtained using more typical virtual screening validation experiments. Guidelines for optimal application of said descriptors are also discussed in the context of the results obtained, as is the potential utility of fingerprint filtering.

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“…However, it would be desirable to perform some kind of rational manipulation of the ligands, with the aim to design ligand molecules that bind in an improved fashion. Most commonly applied under these circumstances are 'indirect' or ligand-based modelling techniques, such as pharmacophore modelling and QSAR (quantitative structure-activity relationship) approaches [10,11].…”

Section: Introductionmentioning

confidence: 99%