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Three-dimensional pharmacophore screening for fentanyl derivatives.

Liu M, Sun Z, Hu W - Neural Regen Res (2012)

Bottom Line: The results of the comparative molecular field analysis model suggested that both steric and electrostatic interactions play important roles.The contributions from steric and electrostatic fields for the model were 0.621 and 0.379, respectively.The pharmacophore model provides crucial information about how well the common features of a subject molecule overlap with the hypothesis model, which is very valuable for designing and optimizing new active structures.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Capital Normal University, Beijing 100048, China.

ABSTRACT
Fentanyl is a highly selective μ-opioid receptor agonist with high analgesic activity. Three-dimensional pharmacophore models were built from a set of 50 fentanyl derivatives. These were employed to elucidate ligand-receptor interactions using information derived only from the ligand structure to identify new potential lead compounds. The present studies demonstrated that three hydrophobic regions, one positive ionizable region and two hydrogen bond acceptor region sites located on the molecule seem to be essential for analgesic activity. The results of the comparative molecular field analysis model suggested that both steric and electrostatic interactions play important roles. The contributions from steric and electrostatic fields for the model were 0.621 and 0.379, respectively. The pharmacophore model provides crucial information about how well the common features of a subject molecule overlap with the hypothesis model, which is very valuable for designing and optimizing new active structures.

No MeSH data available.


The best hypothesis of compound G_2 mapping to model 19.
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Figure 5: The best hypothesis of compound G_2 mapping to model 19.

Mentions: The control parameters used for pharmacophore generation are summarized in Table 4. The overlay alignment of the training compounds which generated model 19 is shown in Figure 5.


Three-dimensional pharmacophore screening for fentanyl derivatives.

Liu M, Sun Z, Hu W - Neural Regen Res (2012)

The best hypothesis of compound G_2 mapping to model 19.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308790&req=5

Figure 5: The best hypothesis of compound G_2 mapping to model 19.
Mentions: The control parameters used for pharmacophore generation are summarized in Table 4. The overlay alignment of the training compounds which generated model 19 is shown in Figure 5.

Bottom Line: The results of the comparative molecular field analysis model suggested that both steric and electrostatic interactions play important roles.The contributions from steric and electrostatic fields for the model were 0.621 and 0.379, respectively.The pharmacophore model provides crucial information about how well the common features of a subject molecule overlap with the hypothesis model, which is very valuable for designing and optimizing new active structures.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Capital Normal University, Beijing 100048, China.

ABSTRACT
Fentanyl is a highly selective μ-opioid receptor agonist with high analgesic activity. Three-dimensional pharmacophore models were built from a set of 50 fentanyl derivatives. These were employed to elucidate ligand-receptor interactions using information derived only from the ligand structure to identify new potential lead compounds. The present studies demonstrated that three hydrophobic regions, one positive ionizable region and two hydrogen bond acceptor region sites located on the molecule seem to be essential for analgesic activity. The results of the comparative molecular field analysis model suggested that both steric and electrostatic interactions play important roles. The contributions from steric and electrostatic fields for the model were 0.621 and 0.379, respectively. The pharmacophore model provides crucial information about how well the common features of a subject molecule overlap with the hypothesis model, which is very valuable for designing and optimizing new active structures.

No MeSH data available.