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Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery
Thesis (PhD (Biochemistry))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2024
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| _version_ | 1867613555622150144 |
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| access_status_str | Open Access |
| author2 | Birkholtz, Lyn-Marie |
| author_browse | Birkholtz, Lyn-Marie |
| author_facet | Birkholtz, Lyn-Marie |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Biochemistry))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/94522 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:00.699Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/94522 Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery Birkholtz, Lyn-Marie u14020590@tuks.co.za Pillay, Nelishia Van Heerden, Ashleigh UCTD Machine learning P. falciparum ECFP Transfer learning Cluster-based undersampling Dual-activity UMAP Sustainable Development Goals (SDGs) SDG-01: No poverty Natural and agricultural sciences theses SDG-01 SDG-03: Good health and well-being Natural and agricultural sciences theses SDG-03 Thesis (PhD (Biochemistry))--University of Pretoria, 2024. Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds’ molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs. This work was supported by the South African Department of Science and Innovation and National Research Foundation South African Research Chairs Initiative Grant (LMB UID:84627). Biochemistry PhD (Biochemistry) Unrestricted Faculty of Natural and Agricultural Sciences 2024-02-13T09:13:12Z 2024-02-13T09:13:12Z 2024-05 2024-02-12 Thesis * A2024 http://hdl.handle.net/2263/94522 https://doi.org/10.25403/UPresearchdata.25205870 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Machine learning P. falciparum ECFP Transfer learning Cluster-based undersampling Dual-activity UMAP Sustainable Development Goals (SDGs) SDG-01: No poverty Natural and agricultural sciences theses SDG-01 SDG-03: Good health and well-being Natural and agricultural sciences theses SDG-03 Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title | Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title_full | Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title_fullStr | Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title_full_unstemmed | Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title_short | Machines against malaria : artificial intelligence classification models to advance antimalarial drug discovery |
| title_sort | machines against malaria artificial intelligence classification models to advance antimalarial drug discovery |
| topic | UCTD Machine learning P. falciparum ECFP Transfer learning Cluster-based undersampling Dual-activity UMAP Sustainable Development Goals (SDGs) SDG-01: No poverty Natural and agricultural sciences theses SDG-01 SDG-03: Good health and well-being Natural and agricultural sciences theses SDG-03 |
| url | http://hdl.handle.net/2263/94522 https://doi.org/10.25403/UPresearchdata.25205870 |