A 3D quantitative structure-activity relationship (3D-QSAR) model for predicting the σ2receptor affinity has been constructed with the aim of providing a useful tool for the identification, design, and optimization of novel σ2receptor ligands. The model has been built using a set of 500 selective σ2receptor ligands recovered from the sigma-2 receptor selective ligand database (S2RSLDB) and developed with the software Forge. The present model showed high statistical quality as confirmed by its robust predictive potential and satisfactory descriptive capability. The drawn up 3D map allows for a prompt visual comprehension of the electrostatic, hydrophobic, and shaping features underlying σ2receptor ligands interaction. A theoretic approach for the generation of new lead compounds with optimized σ2receptor affinity has been performed by means of scaffold hopping analysis. Obtained results further confirmed the validity of our model being some of the identified moieties have already been successfully employed in the development of potent σ2receptor ligands. For the first time is herein reported a 3D-QSAR model which includes a number of chemically diverse σ2receptor ligands and well accounts for the individual ligands affinities. These features will ensure prospectively advantageous applications to speed up the identification of new potent and selective σ2receptor ligands.

Hyphenated 3D-QSAR statistical model-scaffold hopping analysis for the identification of potentially potent and selective sigma-2 receptor ligands

Floresta Giuseppe;Rescifina Antonio
;
Marrazzo Agostino;Dichiara Maria;Pistarà Venerando;Pittalà Valeria;Prezzavento Orazio;Amata Emanuele
2017

Abstract

A 3D quantitative structure-activity relationship (3D-QSAR) model for predicting the σ2receptor affinity has been constructed with the aim of providing a useful tool for the identification, design, and optimization of novel σ2receptor ligands. The model has been built using a set of 500 selective σ2receptor ligands recovered from the sigma-2 receptor selective ligand database (S2RSLDB) and developed with the software Forge. The present model showed high statistical quality as confirmed by its robust predictive potential and satisfactory descriptive capability. The drawn up 3D map allows for a prompt visual comprehension of the electrostatic, hydrophobic, and shaping features underlying σ2receptor ligands interaction. A theoretic approach for the generation of new lead compounds with optimized σ2receptor affinity has been performed by means of scaffold hopping analysis. Obtained results further confirmed the validity of our model being some of the identified moieties have already been successfully employed in the development of potent σ2receptor ligands. For the first time is herein reported a 3D-QSAR model which includes a number of chemically diverse σ2receptor ligands and well accounts for the individual ligands affinities. These features will ensure prospectively advantageous applications to speed up the identification of new potent and selective σ2receptor ligands.
3D-QSAR; Bioisosteric replacements; Forge and Spark software; Scaffold hopping; Sigma receptor; Sigma-2 receptor; Virtual screening; Dose-Response Relationship, Drug; Humans; Ligands; Models, Molecular; Molecular Structure; Receptors, sigma; Quantitative Structure-Activity Relationship; Pharmacology; Drug Discovery3003 Pharmaceutical Science; Organic Chemistry
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/314073
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