[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85770-en":3,"doc-seo-85770-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},85770,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Inverse-IMPRESSION: A Graph-based Platform for Molecular Structure Elucidation from Experimental NMR Spectroscopic Properties","Inverse-IMPRESSION presents a graph-based platform, built on the inverted Graph Transformer Network IMPRESSION-G2, for rapidly reconstructing molecular bonding directly from experimental NMR spectroscopic information. The workflow uses three connected stages: a one-shot bond-connectivity predictor, a structure-correction step that removes uncertain bonds and iteratively reassigns them, and noise-augmented multi-shot ensemble ranking. Using ¹H and ¹³C NMR data, including 2D experiments (COSY, HSQC, HMBC), the method solves 77.8% of molecules up to 30 heavy atoms with simulated data and 53% (10/19) with experimental data, handling complex products up to 480 Da.","# Inverse-IMPRESSION:A Graph-based Platform for MolecularStructure Elucidation from Experimental NMR SpectroscopicProperties\n\nZheqi Jin³,Grace Armitage³,Richard Cox²,Ben Honoré²,Mohammad Golbabaeeᵇand Craig Buttsa,*  \naSchool of Chemistry,University of Bristol,Cantock's Close,Bristol,BS81TS,U.K.  \nbschool of Engineering Mathematics and Technology,University of Bristol,Ada Lovelace Building,Tankard's Cl,Bristol BS81TW*Correspondence to:Craig.Butts@bristol.ac.uk  \nAbstract:Here,we present a platform built on ourinverted Graph Transformer Network,IMPRESSION-  \nG2,which can accurately and rapidly reconstruct molecular bonding directly from experimentalnuclear magnetic resonance(NMR)spectroscopic information.It comprises three interconnectedstages:a one-shot model that predicts bond connectivity between atoms;a structure-correction stagethat corrects the predicted structures by removing uncertain bonds and iteratively reassigning them;noise-augmented multi-shot prediction,generating an ensemble of candidate structures,which areranked to identify the best-fit structure.By integrating arange of¹H and¹³C NMR data,including two-dimensional (2D)experiments such as COSY,HSQC,and HMBC,the inverse-IMPRESSION platformcorrectly identifies the structures of 77.8%of molecules with up to 30 heavy atoms(H,C,N,O and F)using simulated NMR data,and 10 of 19(53%)molecules using experimental NMR data.Theexperimental structures solved have molecular weights of up to 480 Da and are representative of thecomplex structures in synthetic and natural products that routinely challenge chemists.The inverse-IMPRESSION framework thus provides the first effective approach for automated molecular structureelucidation using graph-based machine learning on experimental data.  \n## Introduction\n\nAccurate and rapid molecular structure elucidation is a fundamental task across fields such as organicsynthesis,materials science,and drug discovery.Among available analytical techniques,nuclearmagnetic resonance(NMR)spectroscopy plays a central role due to its ability to provide detailedinformation on local chemical environments,including atomic connectivity,functional groups,andstereochemistry.However,interpreting NMR spectra remains a challenging and time-consumingprocess,with the primary bottleneck arising from the combinatorial explosion of possible molecularrstructures.As molecular size increases,the number of feasible candidate structures growsexponentially,rendering exhaustive enumeration and manual selection impractical on the order of  \n1060molecules.1 Therefore,computer-assisted approaches are essential to systematically manage thiscombinatorial complexity and enable practical and scalable molecular structure elucidation.  \nTraditional Computer-Assisted Structure Elucidation(CASE)systems²-5that determine molecularstructure elucidation from spectroscopic data,especially NMR data,typically rely on logical-combinatorial algorithms to derive molecular fragments from experimental spectra and assemblethem into candidate structures,which are subsequently ranked based on their agreement with theinput data.Modern CASE systems have incorporated density functional theory(DFT),⁶statistical tools7-9and genetic algorithms,1⁰to improve the accuracy and efficiency of structure generation and ranking.However,the reliance on exhaustive structure generation and filtering often results in the ranking ofhighly similar structures at high computational cost,limiting fullautomation for complex molecules.  \nRecent advances in artificial intelligence(AI)and machine learning have driven the emergence of data-driven approaches for molecular structure elucidation.These approaches are at an early stage andrequire large training datasets to effectively capture structure-spectrum relationships,however theyhave the potential to enable fully automated and substantially faster predictions compared totraditional rule-based methods.Current data-driven methods of this type can be broadly categorisedint","cbCaidsy2XyJulC5","https://ap.wps.com/l/cbCaidsy2XyJulC5","pdf",5372485,1,74,"English","en",105,"# Introduction\n## Motivation and challenges in NMR-based structure elucidation\n## Traditional CASE approaches\n## Data-driven machine learning approaches\n## Graph-based representations and prior work","[{\"question\":\"What problem does Inverse-IMPRESSION address in molecular structure elucidation?\",\"answer\":\"It targets the combinatorial explosion and manual burden of interpreting NMR spectra to determine molecular connectivity and structure, especially as molecular size increases.\"},{\"question\":\"How does the platform reconstruct structures from NMR data?\",\"answer\":\"It uses a three-stage pipeline: one-shot bond connectivity prediction, an iterative structure-correction stage that removes uncertain bonds, and noise-augmented multi-shot ensemble generation followed by ranking of candidate structures.\"},{\"question\":\"How does the method perform with simulated versus experimental NMR data?\",\"answer\":\"With simulated NMR data, it correctly identifies 77.8% of molecules up to 30 heavy atoms. With experimental NMR data, it solves 10 of 19 molecules (53%), for structures up to 480 Da.\"}]",1784206146,186,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"inverse-impression-a-graph-based-platform-for-molecular-structure-elucidation-from-experimental-nmr-spectroscopic-properties","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/inverse-impression-a-graph-based-platform-for-molecular-structure-elucidation-from-experimental-nmr-spectroscopic-properties/85770/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does Inverse-IMPRESSION address in molecular structure elucidation?","Question",{"text":74,"@type":75},"It targets the combinatorial explosion and manual burden of interpreting NMR spectra to determine molecular connectivity and structure, especially as molecular size increases.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the platform reconstruct structures from NMR data?",{"text":79,"@type":75},"It uses a three-stage pipeline: one-shot bond connectivity prediction, an iterative structure-correction stage that removes uncertain bonds, and noise-augmented multi-shot ensemble generation followed by ranking of candidate structures.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the method perform with simulated versus experimental NMR data?",{"text":83,"@type":75},"With simulated NMR data, it correctly identifies 77.8% of molecules up to 30 heavy atoms. 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