Mining High‐Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits.

Teckentrup, Andreas; Briem, Hans; Gasteiger, Johann

doi:10.1002/chin.200425234

Cited by 8 publications

(16 citation statements)

References 1 publication

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“…Atom properties and pharmacophore features are widely used for this purpose. Atomic properties include, for example, covalent or van der Waals radii, electronegativities, polarisa-bilities or partial charges [74][75][76]. A pharmacophore is defined as the spatial arrangement of features required for binding such as hydrogen bonds and hydrophobic interactions.…”

Section: Chemical Informationmentioning

confidence: 99%

“…Care has to be taken that there are no gaps or void (lack of information) and that no clusters of molecules are present in any region of the space (redundancy of information). Through the proper selection of descriptors, the molecular space can be derived in a variety of different spaces such as chemical space [43], drug space [150], or activity spaces [28,76]. Usually, molecular properties that are expected to affect binding at a receptor site are chosen along with some description of the molecular topology.…”

Section: Multidimensional Molecular Spacesmentioning

confidence: 99%

“…Using interactive visualisation tools, trends can be easily identified. SOMs have been applied to various tasks of diversity analysis such as subset selection [188,189], dataset comparison [190,191], drug/non-drug discrimination [150,192] and the generation of activity maps [76,193].…”

Section: Dimensionality Reduction Of the Descriptor Spacesmentioning

confidence: 99%

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Diversity in Medicinal Chemistry Space

Gorse¹

2006

CTMC

106

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The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10(13) to 10(180). These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.

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Section: Chemical Informationmentioning

confidence: 99%

Section: Multidimensional Molecular Spacesmentioning

confidence: 99%

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Diversity in Medicinal Chemistry Space

Gorse¹

2006

CTMC

106

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“…Kohonen maps have been applied in various fields during the drug discovery process. Examples are the characterization of compound libraries [67], the analysis of high-throughput screening (HTS) data [68], the mapping of molecular surfaces [67], or the generation of representative subsets [69,70]. For more comprehensive reviews, we refer to the literature [24,56,67].…”

Section: Self Organizing Mapsmentioning

confidence: 99%

Machine Learning Techniques for In Silico Modeling of Drug Metabolism

Fox

Kriegl²

2006

CTMC

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The computational assessment of drug metabolism has gained considerable interest in pharmaceutical research. Amongst others, machine learning techniques have been employed to model relationships between the chemical structure of a compound and its metabolic fate. Examples for these techniques, which were originally developed in fields far from drug discovery, are artificial neural networks or support vector machines. This paper summarizes the application of various machine learning techniques to predict the interaction between organic molecules and metabolic enzymes. More complex endpoints such as metabolic stability or in vivo clearance will also be addressed. It is shown that the prediction of metabolic endpoints with machine learning techniques has made considerable progress over the past few years. Depending on the procedure used, either classification or quantitative prediction is possible for even large and diverse compound sets. Together with the expanding experimental data basis, these in silico methods have become valuable tools in the drug discovery and development process.

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“…Structures of the investigated sesquiterpene lactones representing four structural classes: gemacranolides (1-18), eudesmanolides(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), guaianolides(36)(37)(38)(39)(40)(41)(42)(43), and pseudoguaianolides (44-54).…”

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confidence: 99%

Neural Networks as Valuable Tools To Differentiate between Sesquiterpene Lactones’ Inhibitory Activity on Serotonin Release and on NF-κB

et al. 2008

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Sesquiterpene lactones are the active components of a variety of medicinal plants from the Asteraceae family. They possess biological activities such as the inhibition of NF-kappaB and the release inhibition of the vasoactive serotonin. On the basis of a data set of 54 SLs, we report the development of a quantitative model for the prediction of serotonin release inhibition. Comparing this model with a previous investigation of the target NF-kappaB, structural features necessary for specific compounds could be acquired. Atomic properties encoded by radial distribution function and molecular surface potentials encoded by autocorrelation were used as descriptors. Whereas some descriptors describe the structural requirements for both activities, other descriptors can be used to decide whether an SL is more active to NF-kappaB or to serotonin release. Again, counter propagation neural networks proved to be a valuable tool to establish structure-activity relationships that are necessary for the search for and optimization of lead structures.

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Mining High‐Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits.

Cited by 8 publications

References 1 publication

Diversity in Medicinal Chemistry Space

Diversity in Medicinal Chemistry Space

Machine Learning Techniques for In Silico Modeling of Drug Metabolism

Neural Networks as Valuable Tools To Differentiate between Sesquiterpene Lactones’ Inhibitory Activity on Serotonin Release and on NF-κB

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