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.

Optimal graph-based and Simplified Molecular Input Line Entry System-based descriptors for quantitative structure-activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as sigma1 receptor ligands

ROSSI, DANIELA;COLLINA, SIMONA;
2015-01-01

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/903036
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