Identification of novel series of human CCR1 antagonists

Xie, Yuanyang; Sircar, Ila; Lake, Kirk; Komandla, Mallareddy; Ligsay, Kathleen; Li, Jian; Xu, Kui; Parise, Jason; Schneider, Lisa; Huang, Dingqiu; Liu, Juping; Sakurai, Naoki; Barbosa, Miguel; Jack, R M

doi:10.1016/j.bmcl.2007.09.068

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…The first set (Test set 1, Supporting Information Table S4) comprises 35 endogenous nonpeptide GPCR ligands (targeting 88 GPCR entries) not described in the MDDR data set. The second set (Test set 2, Supporting Information Table S5) was composed of 25 recently described synthetic “druglike” ligands, manually selected from the literature to bind to a diverse set of 28 GPCR targets. − …”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands

Weill

Rognan

2009

J. Chem. Inf. Model.

113

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The present study introduces a novel low-dimensionality fingerprint encoding both ligand and target properties which is suitable to mine protein-ligand chemogenomic space. Whereas ligand properties have been represented by standard descriptors, protein cavities are encoded by a fixed length bit string describing pharmacophoric properties of a definite number of binding site residues. In order to simplify the cavity fingerprint, the concept was applied here to a unique family of targets (G protein-coupled receptors) with a homogeneous cavity description. Particular attention was given to set up data sets of really diverse protein-ligand pairs covering as exhaustively as possible both ligand and target spaces. Several machine learning classification algorithms were trained on two sets of roughly 200000 receptor-ligand fingerprints with a different definition of inactive decoys. Cross-validated models show excellent precision (>0.9) in distinguishing true from false pairs with a particular preference for support vector machine classifiers. When applied to two external test sets of GPCR ligands, the most predictive models were not those performing the best in the previous cross-validation. The ability to recover true GPCR ligands (ligand prediction mode) or true GPCRs (receptor prediction mode) depends on multiple parameters: the molecular complexity of the ligands, the chemical space from which ligand decoys are selected to generate false protein-ligand pairs, and the target space under consideration. In most cases, predicting ligands is easier than predicting receptors. Although receptor profiling is possible, it probably requires a more detailed description of the ligand-binding site. Noteworthy, protein-ligand fingerprints outperform the corresponding ligand fingerprints in mining the GPCR-ligand space. Since they can be applied to a much larger number of receptors than ligand-based fingerprints, protein-ligand fingerprints represent a novel and promising way to directly screen protein-ligand pairs in chemogenomic applications.

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Section: Methodsmentioning

confidence: 99%

Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands

Weill

Rognan

2009

J. Chem. Inf. Model.

113

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“…Specifically, CCR1 has been implicated in the pathogenesis of chronic inflammatory diseases including rheumatoid arthritis and multiple sclerosis. Xie et al [68] describe a series of 4-(4-chlorophenyl)piperidin-4-ols where the CCR1 potency/ binding affinity was increased almost 100-fold by constraining the propyl amine moiety to a piperidine and changing the amide to a sulfonamide. The optimized compound has >100-fold selectivity vs CCR2, CCR3, CCR4, and CCR5 (Scheme 23).…”

Section: Example 23: Ccr1 Antagonistsmentioning

confidence: 98%

Lead Identification

Ellingboe¹,

Gilbert²

2009

Topics in Medicinal Chemistry

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High quality leads provide the foundation for the discovery of successful clinical development candidates, and therefore the identification of leads is an essential part of drug discovery. Many factors contribute to the quality of a lead, including biological, physicochemical, ADME, and PK parameters. The identification of high quality leads, which are needed for successful lead optimization, requires the optimization of all of these parameters. Parallel optimization of all parameters is the most efficient way to achieve the goal of lead identification.

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“…The biological data used in this study were CCR1 inhibitory activity, (in terms of Àlog IC 50 ), of a set of fifty three of various compounds selected from [32]. General chemical structures and the structural details of these compounds as well as biological activity data are given in Table 4.…”

Section: Activity Data and Descriptor Generationmentioning

confidence: 99%