[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117800-en":3,"doc-seo-117800-105":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},117800,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","CREMP - Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning - abstract","Computational and machine learning methods can help model the conformational landscape of macrocyclic peptides, but accurate, fast, and scalable geometry modeling remains difficult due to the unique behavior of macrocycles. CREMP introduces a machine-learning resource generated with CREST, containing 36,198 macrocyclic peptides and high-quality conformational ensembles. The dataset includes nearly 31.3 million unique macrocycle geometries, each labeled with energies from semi-empirical extended tight-binding xTB DFT calculations, supporting improved peptide design and optimization for new therapeutics.","arXiv :2305 .08057v1 [ q-bio .BM] 14 May 2023  \nCREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning  \nColin A. Grambow 1,2* , Hayley Weir1,2 , Christian N. Cunningham3 , Tommaso Biancalani 1 , and Kangway V. Chuang 1,2*  \n1 Department of Artificial Intelligence and Machine Learning, Genentech Research and Early Development, South San Francisco, CA, 94080  \n2 Prescient Design, Genentech Research and Early Development, South San Francisco, CA, 94080  \n3 Department of Peptide Therapeutics, Genentech Research and Early Development, South San Francisco, CA, 94080  \n* corresponding authors: Colin A. Grambow ([grambow.colin@gene.com](grambow.colin@gene.com)) and Kangway V. Chuang ([chuang.kangway@gene.com](chuang.kangway@gene.com))  \nABSTRACT  \nComputational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST) . Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.  \nBackground & Summary  \nIntroduction  \nMacrocyclic peptides are an emerging class of therapeutics in drug discovery.1, 2 Recent advances in affinity selection and display technologies have enabled ultra-large scale screening libraries that identify high-affinity and selective binders for challenging-to-drug proteins.3, 4 Importantly, cyclic peptides occupy a unique biophysical space between small molecules and proteins, and cyclization imparts key properties such as increased proteolytic stability and conformational rigidity.5 The resulting flexible-yet-constrained geometries enable macrocycles to bind to shallow protein surfaces and disrupt protein-protein interactions.6 Intriguingly, the same dynamic conformational behavior that drives high-affinity binding also drives complex chameleonic properties such as permeability.7, 8 Despite their therapeutic potential, this critical conformational behavior is intrinsically challenging to model—accurately modeling geometry and the immense conformational space is difficult to scale. Machine learning methods that can efficiently approximate high-level computational approaches have the potential to enable rational design, yet there currently exist no large-scale datasets of macrocycle structures.  \nCurrent Challenges and Approaches  \nEfficient and accurate conformer generation for macrocycles is challenging due to many factors, including their vast structural diversity, stereochemistry, number of rotatable bonds, and complex intramolecular interactions.7 Furthermore, the challenges of modeling the vast conformational landscape is compounded by complex solvent-dependent effects.9 Computational approaches to macrocycle conformer generation broadly leverage both heuristics-and physics-based algorithms to accurately model and sample molecular geometries. For example, both the open-source cheminformatics library RDKit 10–13 and commercial conformer generation program OpenEye OMEGA Macrocycle 14, 15 combine distance geometry algorithms with molecular force fields 16 for diverse macrocyc","cbCaidahKUqpJsxR","https://ap.wps.com/l/cbCaidahKUqpJsxR","pdf",4550335,1,10,"English","en",105,"# Abstract\n# Background & Summary\n## Introduction\n## Current Challenges and Approaches\n## Datasets to Enable Machine Learning","[{\"question\":\"What problem does CREMP address for macrocyclic peptides?\",\"answer\":\"Accurate, fast, and scalable modeling of macrocycle geometries is still challenging, and large high-quality datasets for training machine learning models are scarce. CREMP provides such a dataset with labeled conformational ensembles.\"},{\"question\":\"How is the CREMP dataset generated?\",\"answer\":\"CREMP is generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). It produces high-quality conformational ensembles for a large set of macrocyclic peptides.\"},{\"question\":\"What information is provided for each geometry in CREMP?\",\"answer\":\"Each geometry is annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations.\"}]","CREMP - Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning - abstract | PDF",1785679636,25,{"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},"cremp-conformer-rotamer-ensembles-of-macrocyclic-peptides-for-machine-learning-abstract","",{"@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/cremp-conformer-rotamer-ensembles-of-macrocyclic-peptides-for-machine-learning-abstract/117800/",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 CREMP address for macrocyclic peptides?","Question",{"text":75,"@type":76},"Accurate, fast, and scalable modeling of macrocycle geometries is still challenging, and large high-quality datasets for training machine learning models are scarce. CREMP provides such a dataset with labeled conformational ensembles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the CREMP dataset generated?",{"text":80,"@type":76},"CREMP is generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). It produces high-quality conformational ensembles for a large set of macrocyclic peptides.",{"name":82,"@type":73,"acceptedAnswer":83},"What information is provided for each geometry in CREMP?",{"text":84,"@type":76},"Each geometry is annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]