Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715386
Title: Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening
Author: Rafati-Afshar, Amir Ali
Awarding Body: Bournemouth University
Current Institution: Bournemouth University
Date of Award: 2017
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Abstract:
Virtual screening in drug discovery involves processing large datasets containing unknown molecules in order to find the ones that are likely to have the desired effects on a biological target, typically a protein receptor or an enzyme. Molecules are thereby classified into active or non-active in relation to the target. Misclassification of molecules in cases such as drug discovery and medical diagnosis is costly, both in time and finances. In the process of discovering a drug, it is mainly the inactive molecules classified as active towards the biological target i.e. false positives that cause a delay in the progress and high late-stage attrition. However, despite the pool of techniques available, the selection of the suitable approach in each situation is still a major challenge. This PhD thesis is designed to develop a pioneering framework which enables the analysis of the virtual screening of chemical compounds datasets in a wide range of settings in a unified fashion. The proposed method provides a better understanding of the dynamics of innovatively combining data processing and classification methods in order to screen massive, potentially high dimensional and overly imbalanced datasets more efficiently.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.715386  DOI: Not available
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