Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations
| dc.contributor.advisor | Nitschke, Geoff Stuart | |
| dc.contributor.author | Maccallum, Robert | |
| dc.date.accessioned | 2023-03-17T12:32:18Z | |
| dc.date.available | 2023-03-17T12:32:18Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-03-17T07:15:54Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Maccallum, R. (2022). <i>Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/37496 | en_ZA |
| dc.identifier.chicagocitation | Maccallum, Robert. <i>"Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations."</i> ., ,Faculty of Science ,Department of Computer Science, 2022. http://hdl.handle.net/11427/37496 | en_ZA |
| dc.identifier.citation | Maccallum, R. 2022. Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/37496 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Maccallum, Robert AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2022 T1 - Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations TI - Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations UR - http://hdl.handle.net/11427/37496 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37496 | |
| dc.identifier.vancouvercitation | Maccallum R. Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations. []. ,Faculty of Science ,Department of Computer Science, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37496 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Computer Science | |
| dc.title | Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |