{"id":{"repo_id":"bournemouth","oai_identifier":"oai:eprints.bournemouth.ac.uk:29248"},"canonical_url":"https://search.dev.ndltd.org/etd/bournemouth/oai:eprints.bournemouth.ac.uk:29248","repository":{"repo_id":"bournemouth","name":"University of Bournemouth","base_url":"http://eprints.bournemouth.ac.uk/cgi/oai2"},"display":{"title":"Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening.","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Rafati-Afshar, Amir Ali"],"institution":"Bournemouth University","degree_name":null,"degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-04","date_published":"2017-04","updated_at":"2026-07-24T01:12:38Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Rafati-Afshar, Amir Ali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-04-11"]},{"key":"dc:date.issued","label":"Date","values":["2017-04"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Science and Technology"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Bournemouth University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://eprints.bournemouth.ac.uk/29248/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://eprints.bournemouth.ac.uk/29248/1/RAFATI%20AFSHAR%2C%20Amir%20Ali_Ph.D._2017.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening."]}]}],"canonical_facts":{"dc:creator":["Rafati-Afshar, Amir Ali"],"dc:date":["2017-04-11"],"dc:date.issued":["2017-04"],"dc:description.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."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://eprints.bournemouth.ac.uk/29248/1/RAFATI%20AFSHAR%2C%20Amir%20Ali_Ph.D._2017.pdf"],"dc:language":["en"],"dc:publisher.department":["Faculty of Science and Technology"],"dc:publisher.institution":["Bournemouth University"],"dc:relation.isreferencedby":["https://eprints.bournemouth.ac.uk/29248/"],"dc:title":["Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening."],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"]},"updated_at":"2026-07-24T01:12:38Z"}