Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening.

Rafati-Afshar, A. A., 2017. Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening. Doctorate Thesis (Doctorate). Bournemouth University.

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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.

Item Type:Thesis (Doctorate)
Additional Information:If you feel that this work infringes your copyright please contact the BURO Manager.
Uncontrolled Keywords:virtual screening; data imbalance; classification; SMOTE
Group:Faculty of Science & Technology
ID Code:29248
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:22 May 2017 11:15
Last Modified:22 May 2017 11:15

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