Chemical Function Queries for 3D Database Search

Greene, Jonathan; Kahn, S. D.; Savoj, H.; Sprague, Peter W.; Teig, Steven L.

doi:10.1021/ci00022a012

Cited by 192 publications

(199 citation statements)

References 9 publications

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“…The default pharmacophore features include positive (P), negative (N), hydrogen-bond acceptor (A), hydrogen-bond donor (D), aromatic (R), and hydrophobic (H) types. For the compounds studied here, the following functional groups were assigned to the H feature using a procedure that has been described in Greene (1994): isopropyl, aromatic halogens, aromatic CH 3 , and methoxy-CH 3 . The location of a given hydrophobic site is a weighted average of the positions of the nonhydrogen atoms in the associated fragment.…”

Section: Methodsmentioning

confidence: 99%

Discovery and Characterization of a G Protein–Biased Agonist That Inhibits β-Arrestin Recruitment to the D2 Dopamine Receptor

Free¹,

Chun²,

Moritz³

et al. 2014

Molecular Pharmacology

118

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A high-throughput screening campaign was conducted to interrogate a 380,0001 small-molecule library for novel D2 dopamine receptor modulators using a calcium mobilization assay. Active agonist compounds from the primary screen were examined for orthogonal D2 dopamine receptor signaling activities including cAMP modulation and b-arrestin recruitment. Although the majority of the subsequently confirmed hits activated all signaling pathways tested, several compounds showed a diminished ability to stimulate b-arrestin recruitment. One such compound (MLS1547; 5-chloro-7-[(4-pyridin-2-ylpiperazin-1-yl)methyl]quinolin-8-ol) is a highly efficacious agonist at D2 receptor-mediated G protein-linked signaling, but does not recruit b-arrestin as demonstrated using two different assays. This compound does, however, antagonize dopaminestimulated b-arrestin recruitment to the D2 receptor. In an effort to investigate the chemical scaffold of MLS1547 further, we characterized a set of 24 analogs of MLS1547 with respect to their ability to inhibit cAMP accumulation or stimulate b-arrestin recruitment. A number of the analogs were similar to MLS1547 in that they displayed agonist activity for inhibiting cAMP accumulation, but did not stimulate b-arrestin recruitment (i.e., they were highly biased). In contrast, other analogs displayed various degrees of G protein signaling bias. These results provided the basis to use pharmacophore modeling and molecular docking analyses to build a preliminary structure-activity relationship of the functionally selective properties of this series of compounds. In summary, we have identified and characterized a novel G protein-biased agonist of the D2 dopamine receptor and identified structural features that may contribute to its biased signaling properties.

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Section: Methodsmentioning

confidence: 99%

Discovery and Characterization of a G Protein–Biased Agonist That Inhibits β-Arrestin Recruitment to the D2 Dopamine Receptor

Free¹,

Chun²,

Moritz³

et al. 2014

Molecular Pharmacology

118

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“…This study uses the program HipHop [6]. The pharmacophoric features identified in HipHop are hydrogen bond donors and acceptors, negative and positive charge centres, and surface accessible hydrophobic regions [7]. As in DISCO, both ligand atoms and projected positions of complementary site atoms are considered as hydrogen bonding features.…”

Section: The Programsmentioning

confidence: 99%

Untitled

Patel

Gillet

Bravi

et al. 2002

Journal of Computer-Aided Molecular Design

130

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SummaryThree commercially available pharmacophore generation programs, Catalyst/HipHop, DISCO and GASP, were compared on their ability to generate known pharmacophores deduced from protein-ligand complexes extracted from the Protein Data Bank. Five different protein families were included Thrombin, Cyclin Dependent Kinase 2, Dihydrofolate Reductase, HIV Reverse Transcriptase and Thermolysin. Target pharmacophores were defined through visual analysis of the data sets. The pharmacophore models produced were evaluated qualitatively through visual inspection and according to their ability to generate the target pharmacophores. Our results show that GASP and Catalyst outperformed DISCO at reproducing the five target pharmacophores.

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“…16 This hypothesis included one oriented hydrogen bond acceptor function (HBA) 17 and four hydrophobic functions. The hypothesis was able to explain the presence of the ambergris odor of these compounds very successfully.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial QSAR of Ambergris Fragrance Compounds

Kovatcheva

Gelbukh

Oloff

et al. 2004

J. Chem. Inf. Comput. Sci.

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A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

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Chemical Function Queries for 3D Database Search

Cited by 192 publications

References 9 publications

Discovery and Characterization of a G Protein–Biased Agonist That Inhibits β-Arrestin Recruitment to the D2 Dopamine Receptor

Discovery and Characterization of a G Protein–Biased Agonist That Inhibits β-Arrestin Recruitment to the D2 Dopamine Receptor

Untitled

Combinatorial QSAR of Ambergris Fragrance Compounds

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