[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124229-en":3,"doc-seo-124229-105":30,"detail-sidebar-cat-0-en-105":95},{"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},124229,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Identification of novel potential hypoxia-inducible factor-1α inhibitors through machine learning and computational simulations","Machine-learning and computation-driven screening was used to discover novel potential inhibitors targeting hypoxia-inducible factor-1α (HIF-1α), a therapeutically relevant regulator in hypoxia-associated cancer biology. A multistage virtual screening pipeline combined machine-learning activity prediction with molecular docking and molecular dynamics, followed by MM-PBSA binding free-energy evaluation against a reference compound. ChEMBL-derived inhibitory activity data supported construction and selection of six models, with a random forest using RDKit descriptors showing the best overall performance. Four candidates were prioritized, and 100 ns simulations indicated Arnidiol and Epifriedelanol produced the most stable HIF-1α interactions.","TYPE Original Research PUBLISHED 12 May 2025  \nDOI 10.3389/fchem.2025.1585882  \nOPEN ACCESS  \nEDITED BY  \nWagdy Mohamed Eldehna, Kafrelsheikh University, Egypt  \nREVIEWED BY  \nKhaled Mohamed Darwish, Suez Canal University, Egypt Ahmed A. Al-Karmalawy, University of Mashreq, Iraq  \n*CORRESPONDENCE  \nJinping Zhang,  \n [zhangjinpingdoctor@126.com](zhangjinpingdoctor@126.com)  \nRECEIVED 01 March 2025  \nACCEPTED 24 April 2025  \nPUBLISHED 12 May 2025  \nCITATION  \nHe Y, Diao S, Hou S, Li T, Meng W and Zhang J (2025) Identification of novel potential hypoxiainducible factor-1α inhibitors through machine learning and computational simulations.  \nFront. Chem. 13:1585882 .  \ndoi: 10.3389/fchem.2025.1585882  \nCOPYRIGHT  \n© 2025 He, Diao, Hou, Li, Meng and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIdentification of novel potential hypoxia-inducible factor-1α inhibitors through machine learning and computational simulations  \nYuxiang He 1, Shuning Diao 1, Shengzhen Hou 1, Taiying Li 1, Wenhui Meng 2 and Jinping Zhang 3*  \n1First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China, 2Third Department of Infectious Diseases, The Fourth People’s Hospital of Zibo, Zibo, China, 3Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China  \nIntroduction: Hypoxia-inducible factor-1α (HIF-1α) has become a significant therapeutic target for breast cancer and other cancers by regulating the expression of downstream genes such as erythropoietin, thereby improving cell survival in hypoxic conditions.  \nMethods: We jointly applied a multistage screening system encompassing machine learning, molecular docking, and molecular dynamics simulations to conduct virtual screening of the “Traditional Chinese Medicine Monomer Library”for potential HIF-1α inhibitors. The virtual screening was conducted in three sequential stages, applying the following selection criteria sequentially: an activity prediction score greater than or equal to 0.8, a stronger binding affinity, and an MM-PBSA binding free energy lower than the reference compound.  \nResults and Discussion: We retrieved 361 compounds with HIF-1α inhibitory activity data from the ChEMBL database for the construction and evaluation of machine learning models. Among the six constructed models, the random forest model based on RDKit molecular descriptor with the optimal comprehensive performance was employed for virtual screening. Ultimately, four compounds were selected for binding mode analyses and 100 ns molecular dynamics simulations. The results showed that the compounds Arnidiol and Epifriedelanol exhibit the most stable interactions with the HIF-1α protein, which can serve as potential HIF-1α inhibitors for future investigations.  \nKEYWORDS  \nhypoxia-inducible factor-1α, virtual screening, machine learning, molecular docking, molecular dynamics simulation  \n1 Introduction  \nHIF-1 is a heterodimeric protein consisting ofthe HIF-1α subunit, which is regulated by oxygen levels, and the constitutively expressed HIF-1β subunit. The HIF-1β subunit plays a critical role in forming the HIF-1 heterodimer, whereas the HIF-1α subunit is primarily responsible for regulating the activity of HIF-1 (Graham and Presnell, 2017) . Both subunits contain a basic helix-loop-helix (bHLH) motif and a Per-ARNT-Sim (PAS) structural domain, which are responsible for binding DNA as well as forming heterodimers. Unlike  \nFrontiers in Chemistry 01 [frontiersin.org](frontiersin.org)  \nHIF-1β, HIF-1α also contains an oxygen-dependent degradation structural domain","cbCaisOH3vVVBaqc","https://ap.wps.com/l/cbCaisOH3vVVBaqc","pdf",5267916,1,19,"English","en",105,"# Introduction\n# Methods\n# Results and Discussion\n## Model Construction and Virtual Screening Pipeline\n## Candidate Selection and Binding/Dynamics Analyses","[{\"question\":\"Why is HIF-1α considered an important therapeutic target?\",\"answer\":\"HIF-1α regulates downstream genes involved in hypoxic adaptation, and its dysregulation is linked to cancers. Inhibiting HIF-1α can interfere with these hypoxia-responsive transcriptional programs.\"},{\"question\":\"What multistage workflow was used to identify potential HIF-1α inhibitors?\",\"answer\":\"The study used a three-stage virtual screening strategy: machine-learning activity prediction, molecular docking to assess binding affinity, and MM-PBSA binding free-energy filtering using a reference compound.\"},{\"question\":\"How were machine-learning models trained and which model performed best?\",\"answer\":\"The authors collected 361 HIF-1α inhibitory-activity compounds from ChEMBL to build and evaluate six models. The random forest model based on RDKit molecular descriptors achieved the optimal comprehensive performance for screening.\"},{\"question\":\"Which compounds showed the most stable interactions with HIF-1α and how was stability assessed?\",\"answer\":\"Arnidiol and Epifriedelanol were selected for binding-mode analysis and then simulated for 100 ns molecular dynamics. The simulations indicated the most stable interactions with the HIF-1α protein, supporting their potential as inhibitors.\"}]","Identification of novel potential hypoxia-inducible factor-1α inhibitors through machine learning and computational simulations | PDF",1785821127,48,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"identification-of-novel-potential-hypoxia-inducible-factor-1-inhibitors-through-machine-learning-and-computational-simulations","",{"@graph":36,"@context":89},[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/identification-of-novel-potential-hypoxia-inducible-factor-1-inhibitors-through-machine-learning-and-computational-simulations/124229/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is HIF-1α considered an important therapeutic target?","Question",{"text":75,"@type":76},"HIF-1α regulates downstream genes involved in hypoxic adaptation, and its dysregulation is linked to cancers. Inhibiting HIF-1α can interfere with these hypoxia-responsive transcriptional programs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What multistage workflow was used to identify potential HIF-1α inhibitors?",{"text":80,"@type":76},"The study used a three-stage virtual screening strategy: machine-learning activity prediction, molecular docking to assess binding affinity, and MM-PBSA binding free-energy filtering using a reference compound.",{"name":82,"@type":73,"acceptedAnswer":83},"How were machine-learning models trained and which model performed best?",{"text":84,"@type":76},"The authors collected 361 HIF-1α inhibitory-activity compounds from ChEMBL to build and evaluate six models. The random forest model based on RDKit molecular descriptors achieved the optimal comprehensive performance for screening.",{"name":86,"@type":73,"acceptedAnswer":87},"Which compounds showed the most stable interactions with HIF-1α and how was stability assessed?",{"text":88,"@type":76},"Arnidiol and Epifriedelanol were selected for binding-mode analysis and then simulated for 100 ns molecular dynamics. 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