[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117114-en":3,"doc-seo-117114-105":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},117114,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Ligand-Protein Binding Affinity Prediction Using Machine Learning Scoring Functions - PhD Thesis Index","This thesis investigates ligand-protein binding affinity prediction through machine learning scoring functions, building a research workflow from model design to evaluation. It covers core machine learning concepts, artificial neural network architectures, training methods including gradient descent and backpropagation, and strategies to address overfitting such as stopping criteria. A dedicated database is constructed using proteins, ligands, complexes, structures, affinity and docking scores, then processed with normalization and feature handling. The state-of-the-art is reviewed, and an MLP scoring function is developed, trained, and benchmarked via horizontal, vertical, and per-target test protocols with performance comparisons and related factors.","UNIVERSITA’ DEGLI STUDI DI CAMERINO  \nSchool of Advanced Studies  \nDOCTORATE COURSE IN  \n“Physics”  \nXXXIV Cycle  \nTITLE OF THE THESIS:  \n“Ligand-Protein Binding Affinity Prediction Using Machine Learning Scoring Functions”  \nPhD Student Supervisor  \nIng. Francesco Pellicani Prof. Sebastiano Pilati  \nCo-supervisor  \nProf. Diego Dal Ben  \nIndex  \nIntroduction ...................................................................................................................... 5  \n1 Machine Learning ....................................................................................................... 10  \n1.1 Artificial intelligence and machine learning ........................................................... 10  \n1.1.1 Artificial Intelligence ....................................................................................... 10  \n1.1.2 Machine learning ............................................................................................ 12  \n1.2 Artificial neural network description ...................................................................... 16  \n1.3 Neural network training method............................................................................22  \n1.3.1 Gradient descent ............................................................................................24  \n1.3.2 Gradients computation in artificial neural networks: backpropagation algorithm................................................................................................................................26  \n1.3.3 Overfitting and underfitting .............................................................................28  \n1.3.4 Stopping criterion ...........................................................................................32  \n1.4 Neural network training strengthening: transfer learning ......................................32  \n1.5 Neural network performance descriptors..............................................................33  \n2 Database .................................................................................................................... 36  \n2.1 Database description............................................................................................36  \n2.1.1 Proteins ..........................................................................................................38  \n2.1.2 Ligand ............................................................................................................43  \n2.1.3 Ligand-protein complex ..................................................................................44  \n2.1.4 Ligand-protein complex structure ...................................................................44  \n2.1.5 Ligand-protein complex affinity and docking score.........................................46  \n2.2 Experimental data ................................................................................................48  \n2.2.1 Experimental data preparation .......................................................................49  \n2.3 Synthetic data.......................................................................................................50  \n2.3.1 Synthetic data preparation .............................................................................51  \n2.4 Data distribution ...................................................................................................53  \n2.5 Database creation result ......................................................................................60  \n3 State of the art ............................................................................................................ 63  \n3.1 Machine learning scoring function ........................................................................63  \n3.2 Database ..............................................................................................................71  \n3.3 Test types ..............................................................","cbCaicS9iHh0JJCd","https://ap.wps.com/l/cbCaicS9iHh0JJCd","pdf",2232622,1,136,"English","en",105,"# Introduction\n# Machine Learning\n## Artificial intelligence and machine learning\n## Artificial neural network description\n## Neural network training method\n## Neural network training strengthening: transfer learning\n## Neural network performance descriptors\n# Database\n## Database description\n## Experimental data\n## Synthetic data\n## Data distribution\n## Database creation result\n# State of the art\n## Machine learning scoring function\n## Database\n## Test types\n## Performance doping factor\n# MLP Scoring function\n## Ligand-protein complex descriptive model\n## MLP scoring function neural network and training protocol choice\n## Test set choice\n# MLP scoring function performance\n## Horizontal test\n## Vertical test\n## Per-target vertical test\n## Performance comparison","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets predicting ligand-protein binding affinity using machine learning scoring functions, and evaluates performance using multiple test types.\"},{\"question\":\"Which machine learning and neural network training concepts are covered?\",\"answer\":\"It explains artificial intelligence and machine learning, neural network structure, training via gradient descent and backpropagation, overfitting/underfitting, stopping criteria, and transfer learning.\"},{\"question\":\"How is the evaluation of the MLP scoring function organized?\",\"answer\":\"Performance is assessed through horizontal, vertical, and per-target vertical tests, followed by performance comparisons.\"}]","Ligand-Protein Binding Affinity Prediction Using Machine Learning Scoring Functions - PhD Thesis Index | PDF",1785673953,343,{"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},"ligand-protein-binding-affinity-prediction-using-machine-learning-scoring-functions-phd-thesis-index","",{"@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/ligand-protein-binding-affinity-prediction-using-machine-learning-scoring-functions-phd-thesis-index/117114/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis targets predicting ligand-protein binding affinity using machine learning scoring functions, and evaluates performance using multiple test types.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and neural network training concepts are covered?",{"text":80,"@type":76},"It explains artificial intelligence and machine learning, neural network structure, training via gradient descent and backpropagation, overfitting/underfitting, stopping criteria, and transfer learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the evaluation of the MLP scoring function organized?",{"text":84,"@type":76},"Performance is assessed through horizontal, vertical, and per-target vertical tests, followed by performance comparisons.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]