From atoms and bonds to three-dimensional atomic coordinates: automatic model builders

Sadowski, Jens; Gasteiger, Johann

doi:10.1021/cr00023a012

Cited by 400 publications

(315 citation statements)

References 13 publications

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“…Deuterium atoms in several Maybridge database entries were manually changed to hydrogen atoms. Protonation of the molecules in the Maybridge database and the 250 inhibitors was performed by generating three-dimensional structures for these molecules with CORINA 26 and then applying a locally modified version of the molecular format interconversion program, Babel. 27 In this computer program, the rules for performing protonation were modified to better reflect the expected ionization state at physiological pH for functional groups including amidine, guanidinium, N-oxide, and tetrazole.…”

Section: Methodsmentioning

confidence: 99%

Virtual Screening of Molecular Databases Using a Support Vector Machine.

Jorissen

Gilson

2005

ChemInform

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The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.

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Section: Methodsmentioning

confidence: 99%

Virtual Screening of Molecular Databases Using a Support Vector Machine.

Jorissen

Gilson

2005

ChemInform

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“…S1]. The 3D structures of the 13 000 compounds thus selected were built using CORINA (19). These structures were tested for interaction with CCR4 by using the GOLD docking program and the GoldScore fitness function (20).…”

Section: Identification Of Potential Lead Molecules Through Structurementioning

confidence: 99%

In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination

Bayry

Tchilian

Davies

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

116

100

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Adjuvants are substances that enhance immune responses and thus improve the efficacy of vaccination. Few adjuvants are available for use in humans, and the one that is most commonly used (alum) often induces suboptimal immunity for protection against many pathogens. There is thus an obvious need to develop new and improved adjuvants. We have therefore taken an approach to adjuvant discovery that uses in silico modeling and structure-based drug-design. As proof-of-principle we chose to target the interaction of the chemokines CCL22 and CCL17 with their receptor CCR4.

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“…These were the semiempirical method AM1 18 as implemented in MOPAC93 (Software release 2002, Stewart, J. J. P.) and the ab initio method B3LYP/6-31G** as provided by the Gaussian 98 (Gaussian 98 revision A.7, Gaussian Inc., Pittsburgh, PA) package. All calculations included geometry optimization, employing CORINA 2001 22,23 to generate initial 3D structures from Simplified Molecular Input Line Entry Specification (SMILES) strings, followed by SYBYL force field geometry optimizations (SYBYL version 6.8, Tripos Associates, St. Louis, MO) before starting quantum chemical calculations. 16 Because no conformational search routines were employed it may well be that, in individual cases, the finally achieved geometry does not represent the energy minimum of the conformational space.…”

Section: Methodsmentioning

confidence: 99%

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

et al. 2005

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The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K ow ). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

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From atoms and bonds to three-dimensional atomic coordinates: automatic model builders

Cited by 400 publications

References 13 publications

Virtual Screening of Molecular Databases Using a Support Vector Machine.

Virtual Screening of Molecular Databases Using a Support Vector Machine.

In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

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