[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119889-en":3,"doc-seo-119889-105":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":20,"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},119889,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT - Dissertation Abstract","Human health faces intensified pressures including cancer, pandemic outbreaks, and antimicrobial resistance, creating urgent demand for new medicines such as peptide-based vaccines and permeable small-molecule antimicrobials. Drug development remains long, complex, and high-risk without success guarantees. Advances in genomics, proteomics, computational biology, and clinical trials increase data scale and complexity, raising requirements for the drug development pipeline. This dissertation evaluates advanced machine learning approaches to accelerate peptide vaccine screening, enhance peptide–MHC binding affinity prediction via structure- and HLA modeling, and identify physicochemical permeability determinants using random forests.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Doctoral Dissertations | Graduate School |\n| --- | --- |\n| 5-2023\u003Cbr>APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT\u003Cbr>Yue Shen\u003Cbr>University of Tennessee, Knoxville, [yshen25@vols.utk.edu](yshen25@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_graddiss](https://trace.tennessee.edu/utk_graddiss)\u003Cbr> Part of the Computational Biology Commons |  |\n\nRecommended Citation  \nShen, Yue, \"APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT. \"PhD diss., University of Tennessee, 2023.  \n[https://trace.tennessee.edu/utk_graddiss/8171](https://trace.tennessee.edu/utk_graddiss/8171)  \nThis Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a dissertation written by Yue Shen entitled \"APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT.\" I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences.  \nJeremy C. Smith, Major Professor  \nWe have read this dissertation and recommend its acceptance: Jerry M. Parks, Scott Emrich, Tongye Shen  \nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nAPPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT  \nA Dissertation Presented for the  \nDoctor of Philosophy  \nDegree  \nThe University of Tennessee, Knoxville  \nYue Shen  \nAbstract  \nHuman health, one of the major topics in Life Science, is facing intensified challenges, including cancer, pandemic outbreaks, and antimicrobial resistance. Thus, new medicines with unique advantages, including peptide-based vaccines and permeable small molecule antimicrobials, are in urgent need. However, the drug development process is long, complex, and risky with no guarantee of success. Also, the improvements in techniques applied in genomics, proteomics, computational biology, and clinical trials significantly increase the data complexity and volume, which imposes higher requirements on the drug development pipeline. In recent years, machine learning (ML) methods were employed to support drug development in various aspects and were shown to be highly effective. Here, we explored the application of advanced ML approaches to empower the development of peptide-based vaccines and permeable antimicrobials. First, the peptide-based vaccines targeting pancreatic cancer and COVID-19 were predicted and screened via multiple approaches. Next, novel structure-based methods to improve the performance of peptide: MHC binding affinity prediction were developed, including an HLA modeling pipeline that provides structures for docking-based peptide binder validation, and hierarchical clustering of HLA I into supertypes and subtypes that have similar peptide binding specificity. Finally, the physicochemical properties governing the permeability of small molecules into multidrug-resistant Pseudomonas aeruginosa cells were selected using a random forest model. In conclusion, the use of machine learning methods could accelerate the drug development process at a lower cost and promote data-based decision-making if used properly.  \nTable of Contents  \nChapter 1 Introduction and General Information............................................................................ 1  \n1.1 Introduction .................................................................................","cbCaibfXienfRqwj","https://ap.wps.com/l/cbCaibfXienfRqwj","pdf",5177540,1,123,"English","en",105,"# Chapter 1 Introduction and General Information\n## 1.1 Introduction\n## 1.2 Major Histocompatibility Complex\n## 1.3 Peptide-based vaccine targeting MHC class I molecules\n## 1.4 Peptide MHC binding affinity prediction\n## 1.5 Multidrug resistance in bacteria","[{\"question\":\"Why is machine learning relevant to drug development in this dissertation?\",\"answer\":\"Drug development is long and risky, while modern biomedical fields generate highly complex and large datasets. The dissertation investigates ML methods to support data-driven decisions and accelerate development.\"},{\"question\":\"How does the dissertation address peptide-based vaccines for diseases like pancreatic cancer and COVID-19?\",\"answer\":\"Peptide-based vaccine candidates targeting pancreatic cancer and COVID-19 are predicted and screened using multiple computational approaches.\"},{\"question\":\"What ML techniques are used to improve peptide–MHC binding affinity prediction and small-molecule permeability?\",\"answer\":\"Structure-based methods and an HLA modeling pipeline support docking-based peptide binder validation, with hierarchical clustering of HLA I into supertypes and subtypes. For permeability, physicochemical properties are selected using a random forest model.\"}]","APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT - Dissertation Abstract | PDF",1785726852,310,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-approaches-to-empower-drug-development-dissertation-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-approaches-to-empower-drug-development-dissertation-abstract/119889/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is machine learning relevant to drug development in this dissertation?","Question",{"text":75,"@type":76},"Drug development is long and risky, while modern biomedical fields generate highly complex and large datasets. The dissertation investigates ML methods to support data-driven decisions and accelerate development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation address peptide-based vaccines for diseases like pancreatic cancer and COVID-19?",{"text":80,"@type":76},"Peptide-based vaccine candidates targeting pancreatic cancer and COVID-19 are predicted and screened using multiple computational approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What ML techniques are used to improve peptide–MHC binding affinity prediction and small-molecule permeability?",{"text":84,"@type":76},"Structure-based methods and an HLA modeling pipeline support docking-based peptide binder validation, with hierarchical clustering of HLA I into supertypes and subtypes. 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