[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121368-en":3,"doc-seo-121368-105":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},121368,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Assisted Odor Assessment Based on Molecular Structures - Dissertation","The research investigates how molecular structures relate to aromatic properties and how machine learning can predict odors for single molecules and complex mixtures. It addresses the challenge that similar structures may produce very different scent perceptions. In addition to aroma prediction, the dissertation models potential toxicity of molecules to support early safety and property assessment in product development. Three ML approaches are introduced, leveraging molecular representations such as 3D electron densities, to improve accuracy and reduce trial-and-error in formulation.","Dissertation  \n2025  \nSatnam Singh  \nMachine Learning Assisted Odor Assessment Based on Molecular Structures  \nMachine Learning Assisted Odor Assessment Based on Molecular  \nStructures  \nDer Medizinischen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg  \nzur  \nErlangung des Doktorgrades  \nDr. rer. biol. hum.  \nvorgelegt von  \nSatnam Singh  \naus Neu Delhi  \nAls Dissertation genehmigt von der Medizinischen Fakultät  \nder Friedrich-Alexander-Universität Erlangen-Nürnberg  \nTag der mündlichen Prüfung: 16.04.2025  \nGutachter/in: Prof. Dr. Jessica Freiherr  \nProf. Dr. Bernard Egger  \n\"Ooh, let’s talk about chemistry. .. \"  \n-Vessel  \nContents  \nAbstract ............................................. i  \nZusammenfassung ....................................... ii  \n1 Introduction ........................................ 1  \n1.1 Goals and Scientific Contribution ........................... 3  \n1.2 Molecular Representations ............................... 4  \n1.3 Molecular Properties .................................. 7  \n1.3.1 Olfaction and Aroma of Molecules ...................... 7  \n1.3.2 Potential Toxicity of Molecules ........................ 9  \n1.4 Machine Learning Algorithms ............................. 10  \n1.4.1 OWSum: Olfactory Weighted Sum ...................... 10  \n1.4.2 Molecular Mixtures ............................... 10  \n1.4.3 3D Electron Density Based Neural Network ................. 11  \n1.5 Molecular Generation ................................. 11  \n1.6 Conclusion ........................................ 13  \n2 Original Publications ................................... 14  \n2.1 OWSum: algorithmic odor prediction and insight into structure-odor relationships 14  \n2.2 Classification of substances by health hazard using deep neural networks and molecular electron densities .............................. 15  \n2.3 Odor Prediction of Whiskies Based on Their Molecular Composition ....... 16  \n2.4 Conference Contributions ............................... 18  \n2.5 Publications in preparation .............................. 18  \n3 Acknowledgments ..................................... 19  \nList of Figures ......................................... 20  \nList of Tables .......................................... 21  \nBibliography .......................................... 22  \nAbstract  \nThe sense of smell is known to significantly influence human experiences, affecting food and product choices, memories and moods. This study explores the intricate relationship between molecular structures and the aromatic properties of molecules through the application of advanced machine learning (ML) techniques and emphasizes the necessity of accurately predicting the odors of both singular molecules and complex mixtures, particularly in industries such as cosmetics and food, where the development of new aromas is crucial.  \nThis research explores the inherent complexities in modeling olfactory properties, recognizing that molecular structures do not always correlate directly with the scent that the molecule elicits. For instance, closely related molecules, i.e., molecules with similar structures may evoke vastly different odors. This challenge underscores the necessity for sophisticated ML algorithms capable of discerning intricate relationships between molecular features and their corresponding olfactory properties, for both, mono-molecules and complex mixtures. This can also be extended to other molecular properties, for example, potential toxicity of molecules especially for food and cosmetic products. Thus, this dissertation specifically addresses the predictive modeling of aroma and potential toxicity of molecules, highlighting the importance of an integrated early safety and property assessment system in product development.  \nA significant contribution of this work includes the development of three machine learning algorithms: the Olfactory Weighted Sum (OWSum), which classifies molecules based on their structural features","cbCairMZ63rYAhsS","https://ap.wps.com/l/cbCairMZ63rYAhsS","pdf",8484073,1,35,"English","en",105,"# Introduction\n## Goals and Scientific Contribution\n## Molecular Representations\n## Molecular Properties\n## Machine Learning Algorithms\n## Molecular Generation\n## Conclusion\n# Original Publications\n## OWSum: algorithmic odor prediction and insight into structure-odor relationships\n## Classification of substances by health hazard using deep neural networks and molecular electron densities\n## Odor Prediction of Whiskies Based on Their Molecular Composition\n## Conference Contributions\n## Publications in preparation\n# Acknowledgments\n## List of Figures\n## List of Tables\n## Bibliography","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To develop machine-learning methods that accurately predict the odors of molecules and complex mixtures, supported by an additional assessment of potential toxicity for product safety.\"},{\"question\":\"Why is odor prediction challenging from molecular structure alone?\",\"answer\":\"Molecular structures do not always correlate directly with the scent a molecule elicits; closely related molecules can evoke very different odors.\"},{\"question\":\"Which machine learning models are proposed in the dissertation?\",\"answer\":\"The work introduces OWSum for olfactory weighted sum-based structure-odor insights, a 2D CNN for aroma prediction in complex whisky mixtures, and a modified 3D UNet (eDen) using 3D electron densities for potential toxicity prediction.\"}]","Machine Learning Assisted Odor Assessment Based on Molecular Structures - Dissertation | PDF",1785735290,88,{"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},"machine-learning-assisted-odor-assessment-based-on-molecular-structures-dissertation","",{"@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/machine-learning-assisted-odor-assessment-based-on-molecular-structures-dissertation/121368/",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},"What is the main goal of the dissertation?","Question",{"text":75,"@type":76},"To develop machine-learning methods that accurately predict the odors of molecules and complex mixtures, supported by an additional assessment of potential toxicity for product safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is odor prediction challenging from molecular structure alone?",{"text":80,"@type":76},"Molecular structures do not always correlate directly with the scent a molecule elicits; closely related molecules can evoke very different odors.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are proposed in the dissertation?",{"text":84,"@type":76},"The work introduces OWSum for olfactory weighted sum-based structure-odor insights, a 2D CNN for aroma prediction in complex whisky mixtures, and a modified 3D UNet (eDen) using 3D electron densities for potential toxicity prediction.","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"]