Scaffold Perception, ComPharmacophore Model Development, And Quantitative Structure-Affinity Relationships Of Sigma Site Ligands

Sigma receptors are endogenous proteins with potential utility in treating psychological disorders, ischemia, the psychological and convulsive effects of drugs of abuse, and as an imaging agent for cancerous tissues, among others. Drug design efforts targeting these receptors have been hindered by a lack of structural information of the receptors themselves. Traditional ligand-based approaches have succeeded in generating many compounds with high affinity, and quite a few with selectivity for σ-1 receptors. There are few selective ligands for use as pharmacological probes for the σ-2 receptor. Much effort has gone into exploring the structure activity relationships of ligands targeting these receptors. A critical review of the existing literature covering pharmacophore development for σ receptors was undertaken with the intent to develop computational models to assist in ligand-based drug design efforts. Inspired by the lack of pharmacophore models with general utility, and confronted by the obstacles of data heterogeneity, a database of σ ligands and their binding affinity data was collected. Cohorts of data collected under similar experimental methodologies were assembled and clustered by measures of scaffold dissimilarity. Multiple-Instance Learning techniques were used to train classification models that differentiated molecules as active or inactive, and to assist in the identification of relevant conformations of σ ligands at their macromolecular targets. Conformations of high-affinity ligands were then used to develop general pharmacophore models as part of a virtual screening approach. Structure-activity relationship models based on virtual screening alignments of known sigma ligands were developed in the search for selective σ-1 and σ-2 receptor probes.

Name thesis:degree_name

Ph.D. in Pharmaceutical Sciences

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biomolecular Sciences

Year dc:date.available

2013

Author dc:creator

• Watson, David Enos

Contributors dc:contributor

• Christopher R. McCurdy
• Mahmoud Elsohly
• Stephen J. Cutler

• Pharmacophore Modeling
• Qsar
• Scaffold Perception
• Sigma
• Virtual Screening
• Pharmacy and Pharmaceutical Sciences

Repository record dc:identifier

https://egrove.olemiss.edu/etd/692

OAI identifier oai:identifier

oai:egrove.olemiss.edu:etd-1691