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Graphical abstract Highlights • A 3D-QSAR model was produced for the FABP4 inhibitors. • A scaffold hopping suggested 3000 potentially active FABP4 inhibitors. • Three synthesized molecules have shown an IC 50 between 3.70 and 5.59 μM. Abstract Following on the recent publication of pharmacologically relevant effects, small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have attracted high interest. FABP4 is mainly expressed in macrophages and adipose tissue, where it regulates fatty acid storage and lipolysis, being also an important mediator of inflammation. In this regard, FABP4 recently demonstrated an interesting molecular target for the treatment of type 2 diabetes, other metabolic diseases and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized. In this paper, a quantitative structure-activity relationship (QSAR) model has been produced, in order to predict the bioactivity of FABP4 inhibitors. The methodology has been combined with a scaffold-hopping approach, allowing to identify three new molecules that act as effective inhibitors of this protein. These molecules, synthesized and tested for their FABP4 inhibitor activity, showed IC 50 values between 3.70 and 5.59 μM, with a high level of agreement with the predicted values. [ABSTRACT FROM AUTHOR]

Two 3D quantitative structure-activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB1 and CB2) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB1 and CB2 ligands. A set of 312 molecules have been used to build the model for the CB1 receptor, and a set of 187 molecules for the CB2 receptor. All of the molecules were recovered from the literature among those possessing measured Ki values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB1 and CB2 ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB1 and CB2 ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB1 and CB2 receptors. [ABSTRACT FROM AUTHOR]

A 3D quantitative structure-activity relationship (3D-QSAR) model for predicting the σ 2 receptor affinity has been constructed with the aim of providing a useful tool for the identification, design, and optimization of novel σ 2 receptor ligands. The model has been built using a set of 500 selective σ 2 receptor 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 σ 2 receptor ligands interaction. A theoretic approach for the generation of new lead compounds with optimized σ 2 receptor 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 σ 2 receptor ligands. For the first time is herein reported a 3D-QSAR model which includes a number of chemically diverse σ 2 receptor 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 σ 2 receptor ligands. [ABSTRACT FROM AUTHOR]

Two 3D quantitative structure–activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB1 and CB2) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB1 and CB2 ligands. A set of 312 molecules have been used to build the model for the CB1 receptor, and a set of 187 molecules for the CB2 receptor. All of the molecules were recovered from the literature among those possessing measured Ki values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB1 and CB2 ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB1 and CB2 ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB1 and CB2 receptors.