Property Distributions: Differences Between Drugs, Natural Products, and Molecules from Combinatorial Chemistry.

Fehér, Miklós; Schmidt, J.

doi:10.1002/chin.200317217

Cited by 52 publications

(66 citation statements)

References 1 publication

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“…The distribution of SlogP values shows that natural products are slightly more hydrophobic than drugs and overall have a slightly larger MW as previously observed for other collections of natural products 48, 49. Compounds in MLSMR have the same median and mean distribution of HBD as drugs and natural products (1 and 1.2, respectively) although with outliers (HBD > 10).…”

Section: Resultssupporting

confidence: 66%

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Repository

Singh

Guha

Giulianotti

et al. 2009

J. Chem. Inf. Model.

142

131

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A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR and natural products collections. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different to current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries are located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

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Section: Resultssupporting

confidence: 66%

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Repository

Singh

Guha

Giulianotti

et al. 2009

J. Chem. Inf. Model.

142

131

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show abstract

“…Three-ring scaffolds are the most common in the version of DNP that Koch et al examined, with the counts of two and four ringed-systems lying within one standard deviation 35. Natural products have the highest average number of rings and marketed drugs the least, with natural product derivatives and combinatorially synthesized chemicals inbetween 37. Our results show generally similar trends, but much less pronounced, since we examine larger collections (except for the drugs).…”

Section: Analysis Of Some Existing Databasessupporting

confidence: 68%

Scaffold Topologies. 2. Analysis of Chemical Databases

Wester

Pollock

Coutsias

et al. 2008

J. Chem. Inf. Model.

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We have systematically enumerated graph representations of scaffold topologies for up to 8-ring molecules and 4-valence atoms, thus providing coverage of the lower portion of the chemical space of small molecules (Pollock et al. 1 ). Here, we examine scaffold topology distributions for several databases: ChemNavigator and PubChem for commercially available chemicals, the Dictionary of Natural Products, a set of 2,742 launched drugs, WOMBAT, a database of medicinal chemistry compounds, and two subsets of PubChem, "actives" and DSSTox comprising toxic substances. We also examined a virtual database of exhaustively enumerated small organic molecules, GDB, 2 and contrast the scaffold topology distribution from these collections to the complete coverage of up to 8-ring molecules. For reasons related, perhaps, to synthetic accessibility and complexity, scaffolds exhibiting 6 rings or more are poorly represented. Among all collections examined, PubChem has the greatest scaffold topological diversity, whereas GDB is the most limited. More than 50% of all entries (13,000,000+ actual and 13,000,000+ virtual compounds) exhibit only 8 distinct topologies, one of which is the non-scaffold topology that represents all treelike structures. However, most of the topologies are represented by a single or very small number of examples. Within topologies, we found that 3-way scaffold connections (3-nodes) are much more frequent compared to 4-way (4-node) connections. Fused rings have a slightly higher frequency in biologically oriented databases. Scaffold topologies can be the first step toward an efficient coarse-grained classification scheme of the molecules found in chemical databases.

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“…To determine the in vitro activity of the 2-AQ natural product, a MIC analysis was performed to quantitatively measure its activity against BCC species and other GNF-GNR [13]. The MIC of 2-AQ for B. cepacia ATCC 25416 was 32 μg/ml, but it was 16 μg/ml for B. cenocepacia J2315 and B. multivorans ATCC 27616.…”

Section: Resultsmentioning

confidence: 99%

Screening a mushroom extract library for activity against Acinetobacter baumannii and Burkholderia cepacia and the identification of a compound with anti-Burkholderia activity

Schwan

Dunek²,

Gebhardt³

et al. 2010

Annals of Clinical Microbiology and Antimicrobials

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BackgroundAcinetobacter baumannii and species within the Burkholderia cepacia complex (BCC) are significant opportunistic bacterial pathogens of humans. These species exhibit a high degree of antibiotic resistance, and some clinical isolates are resistant to all currently available antimicrobial drugs used for treatment. Thus, new drugs are needed to treat infections by these species. Mushrooms could be a potential source for new drugs to treat A. baumannii and BCC infections.MethodsThe aim of this study was to screen a library of crude extracts from 330 wild mushrooms by disk diffusion assays for antibacterial activity against A. baumannii and Burkholderia cepacia in the hope of identifying a novel natural drug that could be used to treat infections caused by these species. Once positive hits were identified, the extracts were subjected to bioassay-guided separations to isolate and identify the active drug molecules. MICs were performed to gauge the in vitro activity of the purified compounds.ResultsOnly three crude extracts (0.9%) had activity against A. baumannii and B. cepacia. Compounds from two of these extracts had MICs greater than 128 μg/ml, and further analyses were not performed. From the third extract, prepared from Leucopaxillus albissimus, 2-aminoquinoline (2-AQ) was isolated. This compound exhibited a modest MIC in vitro against strains from nine different BCC species, including multi-drug resistant clinical isolates (MIC = 8-64 μg/ml), and a weak MIC (128 μg/ml) against A baumannii. The IC50 against a murine monocyte line was 1.5 mg/ml.ConclusionThe small number of positive hits in this study suggests that finding a new drug from mushrooms to treat Gram-negative bacterial infections may be difficult. Although 2-AQ was identified in one mushroom, and it was shown to inhibit the growth of multi-drug resistant BCC isolates, the relatively high MICs (8-128 μg/ml) for both A. baumannii and BCC strains suggests that 2-AQ is not suitable for further drug development in its current form.

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Property Distributions: Differences Between Drugs, Natural Products, and Molecules from Combinatorial Chemistry.

Cited by 52 publications

References 1 publication

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Repository

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Repository

Scaffold Topologies. 2. Analysis of Chemical Databases

Screening a mushroom extract library for activity against Acinetobacter baumannii and Burkholderia cepacia and the identification of a compound with anti-Burkholderia activity

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