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Kroemer, Romano T.; Hecht, Peter; Guessregen, Stefan; Liedl, Klaus R.

doi:10.1023/a:1017061417516

Cited by 7 publications

(2 citation statements)

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“…A homogeneous data set is critical for proper QSAR [13][14][15][16][17][18]. This is based upon the similar property principle which holds that similar compounds will behave in similar ways and, because of this, measures of similarity can be used to compare the properties of compounds quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Brogle

Gund²,

Kyle

2006

CCHTS

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A process has been developed whereby libraries of compounds for lead optimization can be synthesized and screened with greater efficiency using computational tools. In this method, analogues of a lead chemical structure are considered in the form of a virtual library. Less than 1/3 of the library is selected as a training set by clustering the compounds and choosing the centroid of each cluster. This training set is then used to generate a model using PLS regression upon the experimental values from that assay using 1D/2D descriptors. The model is applied to the remaining compounds (the test set) for which assay values are predicted and a rank ordering established. An example of this was a set of 169 PDE4 inhibitors. A predictive model was achieved using a training set of 52 compounds. When applied to the remaining 117 compounds this model allowed a rank ordering of these compounds for synthesis and testing. Selecting the top 33 compounds of the test set gives 78% of the compounds with the desired activity (hits) by synthesizing only 50% of the library, including the training set. Selecting the top 59 of the test set gives 97% of the hits from only 67% of the library. This process succeeds by avoiding two principal weaknesses of 2D descriptors: lack of interpretation and lack of extrapolation. Two principal assumptions of QSAR are shown to be unnecessary; removing descriptor redundancy does not improve fit and a predictive r2 greater than 0.5 is not necessary if rank-ordering is desired.

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Section: Introductionmentioning

confidence: 99%

Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Brogle

Gund²,

Kyle

2006

CCHTS

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“…However, a number of investigations have shown that CoMFA protocols can be modified to provide models with better predictive quality. This includes the improvements in the description of molecules and rules for the alignment of molecules as well as the statistics used. − On the other hand, multidimensional QSARs, namely, Vedani's 4D- and 5D-QSAR concepts or Hopfinger's 4D-QSAR, are new ideas that can improve modeling performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling Steric and Electronic Effects in 3D- and 4D-QSAR Schemes: Predicting Benzoic pK_a Values and Steroid CBG Binding Affinities

Polański

Bąk

2003

J. Chem. Inf. Comput. Sci.

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We conducted a systematic study of the performance of the 3D- and 4D-QSAR schemes in modeling steric and electronic effects. In particular, we compared the CoMFA and Hopfinger's 4D-QSAR schemes, which apply completely different concepts for the generation of the molecular data used for modeling QSAR. Hence, we attempted to predict the pK(a) values of (o-, m-, and p-)benzoic acids which were divided into three subseries in order to simulate different levels of steric and electronic control. The steroids binding to CBG were used as a benchmark series where biological activity is limited by shape factors. Although individual models differ depending upon the individual scheme, generally, both CoMFA and 4D-QSAR appeared to provide comparable results, irrespective of the differences in the coding schemes used for the description. Moreover, a new 4D-QSAR scheme involving a self-organizing neural network was designed. Generally, the SOM scheme that we designed performs comparably to the grid scheme; however, it provides better results for the charge type descriptors, and the robust neuron architecture allows for the decrease of the influence of the molecular superimposition mode.

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Effect of steric molecular field settings on CoMFA predictivity

Mittal

Sorich

2007

J Mol Model

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Cited by 7 publications

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Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Modeling Steric and Electronic Effects in 3D- and 4D-QSAR Schemes: Predicting Benzoic pK_a Values and Steroid CBG Binding Affinities

Effect of steric molecular field settings on CoMFA predictivity

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Untitled

Cited by 7 publications

References 0 publications

Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Modeling Steric and Electronic Effects in 3D- and 4D-QSAR Schemes: Predicting Benzoic pKa Values and Steroid CBG Binding Affinities

Effect of steric molecular field settings on CoMFA predictivity

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Modeling Steric and Electronic Effects in 3D- and 4D-QSAR Schemes: Predicting Benzoic pK_a Values and Steroid CBG Binding Affinities