Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Giuseppe Floresta
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy; Department of Chemical Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Agostino Marrazzo
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Carmela Parenti
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Orazio Prezzavento
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Giovanni Nastasi
Department of Mathematics and Computer Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Maria Dichiara
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Emanuele Amata
Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy; Corresponding author.
The data have been obtained from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) and refined according to the QSAR requirements. These data provide information about a set of 548 Sigma-2 (σ2) receptor ligands selective over Sigma-1 (σ1) receptor. The development of the QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). Data here reported include the regression for σ2 receptor pKi QSAR models. The QSAR model was also employed to predict the σ2 receptor pKi values of the FDA approved drugs that are herewith included.
