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Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations
The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. We investigate methods for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targe...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2023
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| _version_ | 1867614450285019136 |
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| access_status_str | Open Access |
| author | Maccallum, Robert |
| author2 | Nitschke, Geoff Stuart |
| author_browse | Maccallum, Robert Nitschke, Geoff Stuart |
| author_facet | Nitschke, Geoff Stuart Maccallum, Robert |
| author_sort | Maccallum, Robert |
| collection | Thesis |
| description | The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. We investigate methods for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art deep reinforcement learning molecular modification algorithms and, through the integration of molecular docking simulations, apply them to automatically design novel antagonists for the adenosine triphosphate binding site of Plasmodium falciparum phosphatidylinositol 4-kinase, an enzyme essential to the malaria parasite's development within an infected host. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37496 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:52:14.186Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37496 Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations Maccallum, Robert Nitschke, Geoff Stuart Computer Science The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. We investigate methods for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art deep reinforcement learning molecular modification algorithms and, through the integration of molecular docking simulations, apply them to automatically design novel antagonists for the adenosine triphosphate binding site of Plasmodium falciparum phosphatidylinositol 4-kinase, an enzyme essential to the malaria parasite's development within an infected host. 2023-03-17T12:32:18Z 2023-03-17T12:32:18Z 2022 2023-03-17T07:15:54Z Master Thesis Masters MSc http://hdl.handle.net/11427/37496 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Computer Science Maccallum, Robert Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| thesis_degree_str | Master's |
| title | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| title_full | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| title_fullStr | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| title_full_unstemmed | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| title_short | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations |
| title_sort | automated ligand design in simulated molecular docking optimising ligand binding affinity through the application of deep q learning to docking simulations |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/37496 |
| work_keys_str_mv | AT maccallumrobert automatedliganddesigninsimulatedmoleculardockingoptimisingligandbindingaffinitythroughtheapplicationofdeepqlearningtodockingsimulations |