The Different Strategies for Designing GPCR and Kinase Targeted Libraries

Lowrie, Jeffery F.; DeLisle, Robert Kirk; Hobbs, Doug W.; Diller, David J.

doi:10.2174/1386207043328625

Cited by 62 publications

(43 citation statements)

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“…The coverage of this area of chemical space is orders of magnitude better at the fragment level than at even the lead-like level; the 23 fragments are present in 93 of 10 4 or 1% of commercially available fragments, but only 675 of 10 11 , or 10 Ϫ6 % of commercially available lead-like molecules. This will be true for most molecules, the only partial exceptions being those for which there is bias in the library, such as aminergic GPCR ligands (26,27). Said another way, the chances of discovering interesting chemotypes for biological targets is many orders of magnitude higher when targeting molecules in the fragment weight range than even at slightly higher size ranges.…”

Section: Discussionmentioning

confidence: 99%

Docking for fragment inhibitors of AmpC β-lactamase

Teotico

Babaoglu

Rocklin

et al. 2009

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

101

103

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Fragment screens for new ligands have had wide success, notwithstanding their constraint to libraries of 1,000 -10,000 molecules. Larger libraries would be addressable were molecular docking reliable for fragment screens, but this has not been widely accepted. To investigate docking's ability to prioritize fragments, a library of >137,000 such molecules were docked against the structure of ␤-lactamase. Forty-eight fragments highly ranked by docking were acquired and tested; 23 had Ki values ranging from 0.7 to 9.2 mM. X-ray crystal structures of the enzyme-bound complexes were determined for 8 of the fragments. For 4, the correspondence between the predicted and experimental structures was high (RMSD between 1.2 and 1.4 Å), whereas for another 2, the fidelity was lower but retained most key interactions (RMSD 2.4 -2.6 Å). Two of the 8 fragments adopted very different poses in the active site owing to enzyme conformational changes. The 48% hit rate of the fragment docking compares very favorably with ''lead-like'' docking and high-throughput screening against the same enzyme. To understand this, we investigated the occurrence of the fragment scaffolds among larger, lead-like molecules. Approximately 1% of commercially available fragments contain these inhibitors whereas only 10 ؊7 % of lead-like molecules do. This suggests that many more chemotypes and combinations of chemotypes are present among fragments than are available among lead-like molecules, contributing to the higher hit rates. The ability of docking to prioritize these fragments suggests that the technique can be used to exploit the better chemotype coverage that exists at the fragment level.crystallography ͉ drug design ͉ hit rates ͉ chemical space

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Section: Discussionmentioning

confidence: 99%

Docking for fragment inhibitors of AmpC β-lactamase

Teotico

Babaoglu

Rocklin

et al. 2009

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

101

103

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“…* Is our QSAR methodology able to capture such pharmacophore features, and can it be used to design new GPCR-targeted libraries of compounds? * Do the results match the well-known medicinal chemistry hypothesis of privileged chemical scaffolds and pharmacophores for GPCRs [11,14,15]? * Is such a QSAR model able to predict the binding not only to GPCRs that were included in the training experimental data, but also to other GPCRs?…”

Section: Introductionmentioning

confidence: 63%

“…Taking into account these precedents, new approaches capable of better capturing GPCR-behavior of ligand candidates are necessary [11]. In this context, we propose an original ligand-based approach, based on the development of a QSAR model from in vitro binding data concerning GPCRs from families A, B and C. This data was extracted from the BioPrint database, an in house large and homogeneous set which includes more than 2,200 compounds (marketed drugs, compounds which failed in clinical trials, and reference compounds) profiled in house under standardized conditions with respect to more than 170 wellcharacterized in vitro assays (receptors, enzymes, ion channels, cellular function, ADME-T).…”

Section: Introductionmentioning

confidence: 99%

QSAR Strategy and Experimental Validation for the Development of a GPCR Focused Library

Gozalbes¹,

Rolland²,

Nicolaï³

et al. 2005

QSAR Comb. Sci.

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An innovative approach has been developed in order to construct new GPCR focused libraries. Experimental binding data generated in house from 1939 diverse drug and druglike compounds on 40 GPCR targets were used to develop and validate a "global GPCR" QSAR model accounting for pharmacophore features related to a general GPCR-binding behavior. To this end, proprietary 3-D descriptors representing pharmacophore fingerprints of the various conformers of the molecules were used to encode compound structures in a numerical form. Statistical treatment of the data was based on two different approaches, linear regression and predictive neighborhood behavior, and synergy models relying on both these two independent approaches were also developed. The best QSAR model was selected on hand of its statistical parameters (R 2 , RMS) and percentage of correctly predicted compounds on a randomly chosen validation set (20% of the compounds). A diverse GPCR library of 2,400 compounds was prepared by applying the global QSAR model on compounds already synthesized in house, as well as on virtual combinatorial compounds which were then synthesized if predicted to be potential GPCR binders by the model. The set of building blocks used to build combinatorial libraries has been enriched in original "GPCR-like" monomers, specially designed for this purpose according to medicinal chemistry know-how and literature knowledge. To validate our approach, 240 compounds (10%) of this library were randomly chosen and tested on 21 different amine and peptide GPCRs, together with 720 combinatorial compounds from an in house diversity-based hit-seeking library, as a reference. The experimental results on these 960 compounds were analyzed after pooling the compounds into those predicted as GPCR-active vs. inactive (360 and 600 compounds respectively). The average hit rate was found to be 5.5 fold higher for the GPCR predicted compounds and, furthermore, the global QSAR model was able to recognize not only "classical" templates but also original ones, allowing us to identify new GPCR chemotypes interacting with aminergic and peptidergic GPCRs.

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“…Their application relies heavily on good background data such as protein crystal structures and well defined structure -activity relationships (SARs) of any active compound series. A recent review discussed computational approaches for target class design using the protein kinase family to illustrate structure-based target class-focused library design, and the GProtein-Coupled Receptor (GPCR) family to illustrate ligand-based target-class-focused library design [34]. Docking and scoring approaches reported within the literature over the last few years have also been reviewed with examples of drugs whose development was heavily influenced by, or based on, structure-based design and screening strategies, such as HIV protease inhibitors [35].…”

Section: Protein Docking and Pharmacophore-based Selectionsmentioning

confidence: 99%

Compound Selection and Filtering in Library Design

Lumley¹

2005

QSAR Comb. Sci.

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Multiple approaches exist within the literature for the generation of new active compounds against existing or new therapeutic targets, but no technique has yet provided the rich source of high-quality hit and lead compounds needed by the industry. The evolution of ultrahigh-throughput screening (HTS) and fast computer-based virtual highthroughput screening (VHTS), provide two contrasting techniques for the potential identification of new active hit compounds. This article considers the trends adopted in the design of large to small compound libraries and some of the recent technologies that aim to search and select from chemical space in order to focus libraries towards known chemical ligands or biological target structures. The ability to combine multiple approaches against a given therapeutic target remains the best approach for success.

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The Different Strategies for Designing GPCR and Kinase Targeted Libraries

Cited by 62 publications

References 0 publications

Docking for fragment inhibitors of AmpC β-lactamase

Docking for fragment inhibitors of AmpC β-lactamase

QSAR Strategy and Experimental Validation for the Development of a GPCR Focused Library

Compound Selection and Filtering in Library Design

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