5D-QSAR for spirocyclic σ1 receptor ligands by Quasar receptor surface modeling

Oberdorf, Christoph; Schmidt, Thomas J.; Wünsch, Bernhard

doi:10.1016/j.ejmech.2010.03.048

Cited by 32 publications

(14 citation statements)

References 41 publications

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“…The receptor surface consists of many hydrophobic particles leading to many hydrophobic interactions during ligand binding. The large amount of hydrophobic interactions is in good accordance with the hydrophobic "region of bulk tolerance" of the receptor surface in the Glennon model [21].…”

Section: Pharmacophore Modelsmentioning

confidence: 61%

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Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Wünsch

2012

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The existing pharmacophore models for 1 receptor ligands are summarized. A common feature of these models is a basic amino group surrounded by different hydrophobic structural elements. The development of novel spirocyclic σ1 receptor ligands (e.g. 3, 4) is based on these models. Enlargement of the distance between the basic amino group and the "Primary Hydrophobic Region " by attachment of the amino group at the cyclohexane ring (9, 10) did not lead to increased σ1 affinity. However, introduction of an additional aryl moiety (12, 17) resulted in very potent σ1 receptor ligands. The high affinity of these compounds is explained by interaction of the additional aryl moiety with a previously unrecognized hydrophobic pocket of the σ1 receptor protein. The promising σ1 affinity and selectivity of the spirocyclic piperidine 3 led to the fluorinated PET-tracers [18F]18 and [18F]19 with excellent σ1 receptor affinity, receptor selectivity, pharmacokinetic and neuroimaging properties.

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Section: Pharmacophore Modelsmentioning

confidence: 61%

“…Pseudoreceptor model according toOberdorf et al 2010 [21].Top: Superposition of 87 spirocyclic 1 ligands (hydrogens omitted). Properties and colors of the annotation points: orange: aromatic; green: hydrophobic; blue: cationic; purple: H-bond acceptor.…”

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confidence: 99%

Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Wünsch

2012

CPD

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“…The large amount of hydrophobic interactions supports the hypothesis that r 1 ligands interact with the steroid binding domain like region 1 (SBDL1) of the r 1 receptor protein. 51 …”

Section: Pharmacophore Models Of R 1 Ligandsmentioning

confidence: 97%

Design, synthesis and pharmacological evaluation of spirocyclic σ1 receptor ligands with exocyclic amino moiety (increased distance 1)

Rack¹,

Fröhlich²,

Schepmann³

et al. 2011

Bioorganic & Medicinal Chemistry

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“…QSAR models have been used to create models that contribute to the design of optimal hydrophobic substituents, because these groups modulate the potency of ligand affinity. Several QSAR methodologies have been used for these purposes including CoMFA , CoMSIA , 5D‐QSAR , and so on. The use of optimal descriptors is a nice alternative for including simple topological information to describe the structure–activity relationship of this kind of compounds.…”

Section: Resultsmentioning

confidence: 99%

Optimal graph‐based and Simplified Molecular Input Line Entry System‐based descriptors for quantitative structure–activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ₁ receptor ligands

Quesada-Romero

Mena-Ulecia

Zúñiga

et al. 2014

Journal of Chemometrics

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In this investigation, an optimal description of the structure–activity relationship was done for 59 σ1 receptor ligands including arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines (denoted as RC‐33 analogs in this manuscript). We used optimal graph‐based (GBD) and Simplified Molecular Input Line Entry System (SMILES)‐based (SBD) descriptors as molecular characteristics of the structures to explain the differences in σ1 receptor affinity between the ligands. The best graph‐based descriptor model (named HFG‐EC0 in the manuscript) included hydrogen‐filled molecular graphs using the extended connectivity of zero order (EC0). This model has an average correlation coefficient value for external test set of 0.786 with a better statistics when comparing with the best SBD model. The best SBD model (named SSSk in the manuscript) includes only three SMILES elements SSSk and has an average correlation coefficient value for external test set of 0.726. These models identified the molecular features that contribute to a high σ1 receptor ligand affinity. Copyright © 2014 John Wiley & Sons, Ltd.

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5D-QSAR for spirocyclic σ1 receptor ligands by Quasar receptor surface modeling

Cited by 32 publications

References 41 publications

Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Design, synthesis and pharmacological evaluation of spirocyclic σ1 receptor ligands with exocyclic amino moiety (increased distance 1)

Optimal graph‐based and Simplified Molecular Input Line Entry System‐based descriptors for quantitative structure–activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ₁ receptor ligands

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5D-QSAR for spirocyclic σ1 receptor ligands by Quasar receptor surface modeling

Cited by 32 publications

References 41 publications

Pharmacophore Models and Development of Spirocyclic Ligands for &#x3C3;1 Receptors

Pharmacophore Models and Development of Spirocyclic Ligands for &#x3C3;1 Receptors

Design, synthesis and pharmacological evaluation of spirocyclic σ1 receptor ligands with exocyclic amino moiety (increased distance 1)

Optimal graph‐based and Simplified Molecular Input Line Entry System‐based descriptors for quantitative structure–activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ1 receptor ligands

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Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Pharmacophore Models and Development of Spirocyclic Ligands for σ1 Receptors

Optimal graph‐based and Simplified Molecular Input Line Entry System‐based descriptors for quantitative structure–activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ₁ receptor ligands