[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125412-en":3,"doc-seo-125412-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125412,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Applications of Machine Learning and General-Purpose GPU Programming in Computational Drug Discovery - Dissertation","Recent advances in parallel computing and machine learning are transforming computational approaches for studying protein drug targets and identifying compounds that modulate them. This dissertation surveys the context of this shift and presents an in-depth account of methods built on GPU programming, machine learning, and molecular dynamics. The work develops target identification and large-scale screening pipelines, including five case studies spanning variational autoencoders, kinetic analyses, graph neural networks, and active learning to improve ligand selection and pipeline performance.","Applications of Machine Learning and General-Purpose GPU programming in Computational Drug Discovery  \nSam Alexander Martino  \nA dissertation submitted is partial fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nof  \nUniversity College London  \nDepartment of Physics and Astronomy University College London  \nI, Sam Alexander Martino, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nAbstract  \nRecent advances in parallel computing and machine learning are revolutionising the computational techniques used to investigate potential protein drug targetsand identify compounds capable of modulating them. This work provides an overview of the context surrounding this revolution, along with an in-depth discussion of my work applying and developing these methods. It introduces the theory behind graphics processing units, machine learning, and molecular dynamics. Further work is categorised into 2 chapters, focusing on applications of these for investigating targets or screening ligands. They contain 5 Case Studies relating to various stages of drug discovery, highlighting my research contributions to the area. Target Identification and Investigation introduces methodologies used to analyse both experimental and simulated protein data, before focusing on 3 projects in this area. Case Study 1 focuses on applying variational autoencoders to learn the conformational space of BRAF activation segments from experimental structures. Case Study 2 analyses kinetic data on NSP13, a protein vital to SARS-CoV-2, and attempts to simulate the systems critical translocation function. Case Study 3 introduces a novel graph neural network based strategy able tooptimise a Markov chain clustering by preserving Kemeny’s constant. Large scale screening covers working with small molecules at large scales, explaining how they are processed and evaluated for potential as drugs. Case Study 4 combines several of the introduced methods to build a drug discovery pipeline aimed at finding compounds for NSP13 in the CACHE2 drug discovery challenge, resulting in experimental confirmation of a novel binder. Case Study 5 takes this pipeline and applies it to RAS, another prolific oncogene, and evaluates the beneficial performance of active learning strategies in improving ligand selection.  \nImpact Statement  \nDrug discovery is one of the most important research topics of our time. Identifying new treatments for diseases or providing safer alternatives to existing strategies aims to improve public healthcare and alleviate suffering. The contents of this work provide effective methods for improving our computational capability to both investigate drug targets and discover new small compound based treatments. It also covers practical applications of such methods to a variety of protein targets, crucial to better understanding them and fighting diseases.  \nThis includes two targets in the MEPK/ERK pathway, a signalling cascade featuring prolific mutations across cancers. Work outlines a working basis for investigating the structural diversity of BRAF, a kinase with many unanswered questions concerning its dynamics and activation. This not only provides a method for better understanding BRAF, but also a strategy capable of generalising to other targets with clear functional motifs and many existing experimental structures. Furthering this study can help to solve current issues, such as paradoxical and monomeric activation. The other target in this cascade, RAS, is the most prolific oncogenic signalling protein known. Effective treatments for mutated RAS have so far been mostly limited to one particular cysteine mutation, and have failed to generalise to other more common mutations. For this system, a novel machine learning enabled drug discovery pipeline was constructed for a lesser treated mutation. Enabling the discovery compounds ","cbCaidV30dqozAWV","https://ap.wps.com/l/cbCaidV30dqozAWV","pdf",29592116,1,398,"English","en",105,"# Abstract\n# Impact Statement\n## Target identification and protein system studies\n## Drug discovery pipelines and case studies\n# Acknowledgements","[{\"question\":\"What core computational technologies does the dissertation introduce?\",\"answer\":\"It introduces the theory behind general-purpose GPU programming, machine learning, and molecular dynamics, linking them to modern drug discovery workflows.\"},{\"question\":\"How is the work organized across applications in drug discovery?\",\"answer\":\"It is structured into two main chapters focused on target investigation and ligand screening, supported by five case studies covering different stages of drug discovery.\"},{\"question\":\"What outcomes and contributions are highlighted in the dissertation impact statement?\",\"answer\":\"The impact statement emphasizes improved computational capability to investigate targets and discover small-compound treatments, including method development such as variational and graph neural network approaches and evaluated screening strategies.\"}]","Applications of Machine Learning and General-Purpose GPU Programming in Computational Drug Discovery - 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