[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127431-en":3,"doc-seo-127431-105":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},127431,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Classifying Host-Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning","Elucidating the topology of host-guest complexes is essential for rational design of supramolecular assemblies. A data-driven framework combines ion mobility–mass spectrometry (IMS–MS), density functional theory (DFT) feature construction, and machine learning to predict and classify binding modes of 1:1 complexes formed between cucurbit[6]uril (CB6) and diamine guests. A regression model trained on DFT-derived descriptors and experimentally measured collisional cross sections (CCS) accurately forecasts CCS values. The predictions separate into two groups corresponding to inclusion and exclusion topologies, showing that DFT-featurization and IMS–MS capture host–guest topology and support data-driven molecular-flask design.","CLASSIFYING HOST-GUEST TOPOLOGY WITH ION MOBILITY-MASS SPECTROMETRY AND MACHINE LEARNING  \nQuentin Duez(a),* , Charlotte Lefebvre(a) , Julien De Winter(a) , Jérôme Cornil(b) , Pascal Gerbaux(a)  \n(a) Organic Synthesis and Mass Spectrometry Laboratory, University of Mons, Place du Parc 23, 7000 Mons, Belgium.  \n(b) Laboratory for Chemistry of Novel Materials, University of Mons, Place du Parc 23, 7000 Mons, Belgium.  \n*Email: [quentin.duez@umons.ac.be](quentin.duez@umons.ac.be)  \nABSTRACT: Elucidating the topology of host-guest complexes is essential for the rational design of supramolecular assemblies. Building on the recent success of data-driven approaches, we evaluate the combination of ion mobility–mass spectrometry (IMS–MS), density functional theory (DFT) featurization , and machine learning to predict and classify the binding modes of 1:1 complexes formed between cucurbit[6]uril (CB6) and diamine guests. Training a regression model with DFT-derived molecular descriptors and experimentally determined collisional cross sections (CCS) enables to predict the CCS of host-guest complexes with a diverse set of diamine guests. The predicted values naturally separate in two distinct groups corresponding respectively to inclusion and exclusion complexes, thereby enabling topology classification. This approach demonstrates that DFT-featurization and IMS–MS data capture well host–guest topology and provide a framework for the data-driven design of supramolecular assemblies.  \nEnzymes are generally considered to be the hallmark of reactivity under confinement , catalysing challenging reactions with remarkable efficiency and selectivity through interactions between their active pocket and the substrates. 1, 2 However, the intrinsic specificity of enzymes limits their broader applicability.3 The demand for catalysts that retain the activity of enzymes through confinement effects, while offering a greater versatility, has drawn a significant interest in the development of artificial nanoenvironments featuring emergent behavior.3, 4 These so-called ‘molecular flasks’3 alter chemical reactivity through confinement, either by encapsulating reactants within the cavity of containers such as cryptands,5 cucurbiturils,6 porphyrin cages,7 coordination cages,8 hydrogen-bonded capsules,9 or by constraining them on a surface.10 Conducting reactions within confined spaces can modulate chemical reactivity by increasing reaction rates, 3 stabilizing reactive intermediates3 or improving reaction selectivity.8, 11-15  \nIn this context , elucidating whether a guest binds within the cavity of a molecular flask presents a significant analytical challenge. For this purpose , ion mobility-mass spectrometry (IMS-MS) stands out as a particularly powerful tool.16-18 Briefly, ion mobility spectrometry separates gaseous ions based on their mobility in a buffer gas under the influence of an electric field, which effectively enables to probe their size and shape as reflected by their collisional cross section (CCS) . Combined with mass spectrometry, which gives access to the determination of complex stoichiometries based on detected m/z ratios , IMS-MS is a tool of choice for probing host-guest topology, typically the formation of isomeric inclusion (IN) vs exclusion (OUT) complexes.19-24 Furthermore , IMS-MS combines high sensitivity with rapid analysis times, making it well-suited for detecting transient species within complex mixtures.25 Traditionally , elucidating complex topology relies (i) on a priori knowledge of whether a given guest fits within the host cavity or; (ii) on comparing experimental CCS values with theoretical estimates obtained from atomistic simulations.26-29  \nIn recent years, machine learning (ML) has been increasingly applied in organic chemistry to predict whether a reaction will occur30 and to estimate reaction efficiency under varying conditions, such as changes in the substrate concentration, temperature and/or in the presence of add","cbCail9RyzZUpWlX","https://ap.wps.com/l/cbCail9RyzZUpWlX","pdf",1123911,1,10,"English","en",105,"# Abstract\n# Background: Molecular flasks and enzyme-like confinement\n# Analytical challenge: Determining inclusion vs exclusion\n# IMS–MS and CCS-based topology probing\n# Machine learning in organic chemistry and DFT-informed descriptors\n# Model system: CB6 and protonated diamine guests\n# Workflow: Training regression to classify binding modes","[{\"question\":\"How does the approach combine IMS–MS with machine learning to classify host-guest topology?\",\"answer\":\"IMS–MS provides CCS measurements for host-guest complexes, while a machine learning regression model is trained using DFT-derived molecular descriptors to predict CCS and separate complexes into inclusion and exclusion groups.\"},{\"question\":\"What host and guests are used as model complexes?\",\"answer\":\"The host is cucurbit[6]uril (CB6) and the guests are protonated diamine molecules forming 1:1 complexes with CB6.\"},{\"question\":\"What data sources are used to train the CCS prediction model?\",\"answer\":\"The model is trained on DFT-derived molecular descriptors for the guests and experimentally determined collisional cross sections (CCS) from IMS–MS measurements.\"}]","Classifying Host-Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning | 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does the approach combine IMS–MS with machine learning to classify host-guest topology?","Question",{"text":75,"@type":76},"IMS–MS provides CCS measurements for host-guest complexes, while a machine learning regression model is trained using DFT-derived molecular descriptors to predict CCS and separate complexes into inclusion and exclusion groups.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What host and guests are used as model complexes?",{"text":80,"@type":76},"The host is cucurbit[6]uril (CB6) and the guests are protonated diamine molecules forming 1:1 complexes with CB6.",{"name":82,"@type":73,"acceptedAnswer":83},"What data sources are used to train the CCS prediction model?",{"text":84,"@type":76},"The model is trained on DFT-derived molecular descriptors for the guests and experimentally determined collisional cross sections (CCS) from IMS–MS 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