[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123515-en":3,"doc-seo-123515-105":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},123515,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Dense Sense - a novel approach utilizing electron density augmented machine learning paradigm to understand the complex odour landscape","Olfaction is a complex biological process in which multiple nasal receptors work together to detect odorant molecules, yet structure–activity relationships remain hard to decipher because crystallizing odor receptors is extremely challenging. This study explores a synergy between quantum-mechanics information and graph neural networks to improve prediction of odor perception. The results highlight the feasibility of deriving odor understanding directly from quantum data, providing a novel machine-learning pathway for investigating olfaction mechanisms.","Open Access Article . Pu on 08 Octoberblished 2025. Downloaded on 2/2/2026 12:30: 14 PM .  \nDigital  \nDiscovery  \nPAPER  \nView Article Online View Journal | View Issue  \nCite this: Digital Discovery, 2025, 4, 3339  \nReceived 23rd May 2025  \nAccepted 21st September 2025 DOI: 10.1039/d5dd00224a[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nDense Sense: a novel approach utilizing electron density augmented machine learning paradigm to understand the complex odour landscape  \nPinaki Saha,  *a Mrityunjay Sharma,bcd Sarabeshwar Balaji,e Aryan Amit Barsainyan,f Ritesh Kumar,  *bc Volker Steuber  a and Michael Schmuker  a  \nOlfaction is a complex process where multiple nasal receptors interact to detect specific odorant molecules. Elucidating structure–activity-relationships for odorants and their receptors remains difficult since crystallization of the odor receptors is an extremely difficult process. Therefore, ligand-based approaches that leverage machine learning remain the state of the art for predicting odorant properties for molecules, such as the graph neural network approach used by Lee et al. In this paper we explored how information from quantum mechanics (QM) could synergistically improve the results obtained with the graph neural network. Our findings underscore the possibility of this methodology in predicting odor perception directly from QM data, offering a novel approach in the machine learning space to understand olfaction.  \nIntroduction  \nOlfaction is a vital sense for perceiving the world that is crucial to the survival of many animals, [e.g.](e.g. in)[ in](e.g. in) foraging, mating and detecting prey and predators. It also plays an important role inhuman life, [e.g.](e.g. to)[ to](e.g. to) detect hazards or maintaining hygiene. Although it plays such an important role, the intricacies of the olfaction process are not well understood. In the case of olfaction in humans, olfactory perception involves about 802 genes which encode for the ORs, out of which 388 genes are functional receptors while the remaining 414 are reported to be pseudogenes.5  \nThe exploration of these receptors using structural biology tools has been challenging due to their high genetic variability and limited expression in in vitro systems. Their instability during the isolation process makes them difficult to crystallize.1–3 Additionally, the binding of odorants to the ORs isnot very straightforward. Individual ORs can recognize multiple odorants while a single odorant can elicit responses from multiple receptors, resulting in a complex scheme for odorant recognition.6 The complexity of odorant-OR binding impedes  \na University of Hertfordshire, UH Biocomputation Group, UK. E-mail: p.saha3@herts. [ac.uk](ac.uk)  \nbCSIR-Central Scienti􀀁c Instruments Organisation, Sector 30-C, Chandigarh-160030, India  \ncAcademy of Scienti􀀁c and Innovative Research (AcSIR), Ghaziabad-201002, India dDepartment of Higher Education, Shimla-171001, Himachal Pradesh, IndiaeIndian Institute of Science Education and Research Bhopal (IISERB), Madhya Pradesh- 462066, India  \nfNational Institute of Technology Karnataka, Surathkal, Karnataka-575025, India  \nstructural based understanding of the odorant binding process. Nonetheless, there is some notable research going on concerning the structural biology aspect of olfaction. Utilizing CryoEM, researchers have been able to elucidate the structure of a single human olfactory receptor:7 OR51E2 . In the case of OR51E2 receptor, size selectivity has been observed for carboxylic acid based ligands. Short chain linear carboxylic acids were shown to bind better to OR51E2 receptor compared to their long chain counterparts.3 Recently using the AI based homology modelling tool Alphafold and molecular dynamics (MD) simulation, researchers have tried to elucidate the structure of different ORs present in the human nasal epithelium.8  \nIn contrast to the structure based approach, we have the ligand based approach where we solely focus on f","cbCaiiydHhIZweEo","https://ap.wps.com/l/cbCaiiydHhIZweEo","pdf",3355749,1,12,"English","en",105,"# Introduction\n## Challenges in decoding odorant–receptor recognition\n## Graph neural networks for odor labeling\n## Ligand-based and quantum-assisted learning approach","[{\"question\":\"Why are traditional structure–activity-relationship studies difficult for odorant receptors?\",\"answer\":\"Crystallization of odor receptors is extremely difficult, making structural understanding hard to obtain.\"},{\"question\":\"How does this work improve upon a graph neural network approach for odor prediction?\",\"answer\":\"It combines quantum mechanics (QM) information with graph neural network learning to enhance predictive performance.\"},{\"question\":\"What modeling task is the graph neural network used for in odor perception research?\",\"answer\":\"The graph neural network framework predicts odor labels (classification) or related properties through embedding-based learning from molecular graphs.\"}]","Dense Sense - a novel approach utilizing electron density augmented machine learning paradigm to understand the complex odour landscape | PDF",1785817029,30,{"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},"dense-sense-a-novel-approach-utilizing-electron-density-augmented-machine-learning-paradigm-to-understand-the-complex-odour-landscape","",{"@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/dense-sense-a-novel-approach-utilizing-electron-density-augmented-machine-learning-paradigm-to-understand-the-complex-odour-landscape/123515/",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","2026-08-04",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},"Why are traditional structure–activity-relationship studies difficult for odorant receptors?","Question",{"text":75,"@type":76},"Crystallization of odor receptors is extremely difficult, making structural understanding hard to obtain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this work improve upon a graph neural network approach for odor prediction?",{"text":80,"@type":76},"It combines quantum mechanics (QM) information with graph neural network learning to enhance predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling task is the graph neural network used for in odor perception research?",{"text":84,"@type":76},"The graph neural network framework predicts odor labels (classification) or related properties through embedding-based learning from molecular graphs.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]