Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.652464
Title: Database-mining : EDULISS : a descriptor based approach
Author: Hinton, Andrew C.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2005
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Abstract:
New developments in computer-based (‘in-silico’) technologies, combined with the exponential growth of publicly available information on potential therapeutic targets, provide a timely opportunity to develop a descriptor-based, virtual screening platform to fast-track ligand selection. Edinburgh University Ligand Selection System, known as EDULISS, aims to meet the database mining needs of the academic community through establishing a large depository of molecular descriptor values relating to over 1.5 million freely available chemicals. Embedding information from over 1500 different topological geometric, physiochemical and toxicological molecular descriptors in a ‘query-able’ MySQL database, provides a method to establish an ADMET (absorption, distribution, metabolism, excretion, and toxicity) context to these otherwise obscure descriptors. EDULISS has been applied to screen 16 prominent supplier chemical catalogues, identifying correlations and cut-off limits between descriptors used in traditional rule-based screens (e.g. rule of five, lead-like & rule of three) and those used in OSAR studies, that show strong correlation to chemical properties mediating protein-ligand interactions. Newly developed substructure and similarity searches, which are reliant on molecular descriptor values stored in EDULISS, have identified a wider pool of tetraethyl ammonium (TEA) based components as potential inhibitors for the aquaporin family of orphan proteins. Combined use of EDULISS in conjunction with molecular docking packages has provided modelling results supporting experimental evidence that suggests an alternative binding position of larger molecular weight TEA analogue inhibitors of AQP1. EDULISS has shown to be a novel chemical informatics tool for the ‘bulk’ analysis of molecular descriptors associated with large compendiums of ‘in-silico’ chemicals with the potential for establishing new rule-based database mining techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.652464  DOI: Not available
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