Atom Type Preferences, Structural Diversity, and Property Profiles of Known Drugs, Leads, and Nondrugs: A Comparative Assessment

Viswanadhan, Vellarkad N.; Rajesh, H; Balaji, V.

doi:10.1021/co2000168

Cited by 11 publications

(4 citation statements)

References 41 publications

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“…We could also observe from Figure how the property-distributions ared fitted to a normal distribution (see also the Supporting Information), knowing that there is interest in statistical interpretation of drug-likeness or to create more accurate filters before further QSAR studies …”

Section: Resultsmentioning

confidence: 99%

Efficient One-Pot, Three-Component Synthesis of a Library of Pyrrolo[1,2-c]pyrimidine Derivatives

Georgescu¹,

Georgescu²,

Popa

et al. 2012

ACS Comb. Sci.

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Herein is reported a simple and efficient one-pot three-component synthesis of pyrrolo[1,2-c]pyrimidine derivatives starting from various substituted pyrimidines, 2-bromoacetophenones, and electron deficient alkynes in epoxides acting both as reaction medium and HBr scavanger. This method proved to be very lucrative and avoids formation of ylide inactivation products. The synthesis represents an environmentally benign alternative to classical methods. The new library of compounds was briefly characterized regarding the improved Lipinski rule to asses the potential drug-likeness of the compounds. The majority of compounds are statisfing the Lipinski rule.

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Section: Resultsmentioning

confidence: 99%

Efficient One-Pot, Three-Component Synthesis of a Library of Pyrrolo[1,2-c]pyrimidine Derivatives

Georgescu¹,

Georgescu²,

Popa

et al. 2012

ACS Comb. Sci.

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“…Various indices have been previously developed to describe a chemical library, especially to describe and quantify their chemical diversity. − ,,,− Twenty-two of these indices were implemented in our study. Our goal is to select the index(ices) satisfying the rules of an RDI and allowing the best description and characterization of the chemical diversity of libraries through the DRCS methodology.…”

Section: Methodsmentioning

confidence: 99%

Visual Characterization and Diversity Quantification of Chemical Libraries: 2. Analysis and Selection of Size-Independent, Subspace-Specific Diversity Indices

Colliandre

Guilloux

Bourg

et al. 2012

J. Chem. Inf. Model.

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High Throughput Screening (HTS) is a standard technique widely used to find hit compounds in drug discovery projects. The high costs associated with such experiments have highlighted the need to carefully design screening libraries in order to avoid wasting resources. Molecular diversity is an established concept that has been used to this end for many years. In this article, a new approach to quantify the molecular diversity of screening libraries is presented. The approach is based on the Delimited Reference Chemical Subspace (DRCS) methodology, a new method that can be used to delimit the densest subspace spanned by a reference library in a reduced 2D continuous space. A total of 22 diversity indices were implemented or adapted to this methodology, which is used here to remove outliers and obtain a relevant cell-based partition of the subspace. The behavior of these indices was assessed and compared in various extreme situations and with respect to a set of theoretical rules that a diversity function should satisfy when libraries of different sizes have to be compared. Some gold standard indices are found inappropriate in such a context, while none of the tested indices behave perfectly in all cases. Five DRCS-based indices accounting for different aspects of diversity were finally selected, and a simple framework is proposed to use them effectively. Various libraries have been profiled with respect to more specific subspaces, which further illustrate the interest of the method.

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“…Further, the exhibited diversity of chemical and physical properties of compounds within the metabolome makes true metabolomics (simultaneous quantification of all metabolites) unattainable with current instrumental capabilities [1,18,23]. As an illustration, the limited molecular-weight distribution of the metabolome prohibits a comprehensive and detailed analysis by mass spectroscopy and generally requires the additional use of chromatography [2,24].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

322

388

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Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

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Atom Type Preferences, Structural Diversity, and Property Profiles of Known Drugs, Leads, and Nondrugs: A Comparative Assessment

Cited by 11 publications

References 41 publications

Efficient One-Pot, Three-Component Synthesis of a Library of Pyrrolo[1,2-c]pyrimidine Derivatives

Efficient One-Pot, Three-Component Synthesis of a Library of Pyrrolo[1,2-c]pyrimidine Derivatives

Visual Characterization and Diversity Quantification of Chemical Libraries: 2. Analysis and Selection of Size-Independent, Subspace-Specific Diversity Indices

Multivariate Analysis in Metabolomics

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