[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127799-en":3,"doc-seo-127799-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127799,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Application of machine learning for predicting G9a inhibitors","G9a is an epigenomic regulator whose activity is strongly influenced by cellular substances, making it important to assess compound effects during biochemical development. The study develops a cost-effective machine learning model to predict whether a compound acts as an active G9a inhibitor. Using quantitative high-throughput screening data from PubChem (about 350,000 compounds) and multiple dataset variations, six classifiers are evaluated. The best configuration combines a reduced feature set with a random forest, achieving up to 90% accuracy and providing feature-importance insights.","UWL REPOSITORY  \n[repository.uwl.ac.uk](repository.uwl.ac.uk)  \nApplication of machine learning for predicting G9a inhibitors  \nIvanova, M.L., Russo, N., Djaid, N and Nikolic, Konstantin ORCID: [https://orcid.org/0000-0002](https://orcid.org/0000-0002)- 6551-2977 (2024) Application of machine learning for predicting G9a inhibitors. Digital Discovery. [http://dx.doi.org/10.1039/D4DD00101J](http://dx.doi.org/10.1039/D4DD00101J)  \nThis is a University of West London scholarly output.  \nContact [open.research@uwl.ac.uk](open.research@uwl.ac.uk) if you have any queries.  \nAlternative formats: If you require this document in an alternative format, please contact: [open.access@uwl.ac.uk](open.access@uwl.ac.uk)  \n[Copyright](Copyright:)[:](Copyright:) [CC. BY. NC license]  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy: If you believe that this document breaches copyright, please contact us at [open.research@uwl.ac.uk](open.research@uwl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nOpen Access Article . Pu on 02 Septemberblished 2024. Down on 9/24/2024loaded 9:40:48 AM .  \nDigital  \nDiscovery  \nPAPER  \nView Article Online View Journal  \nCite this: DOI: 10 .1039/d4dd00101j  \nReceived 10th April 2024  \nAccepted 20th August 2024 DOI: 10.1039/d4dd00101j[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nApplication of machine learning for predicting G9a inhibitors†‡  \nMariya L. Ivanova,  * Nicola Russo,  Nadia Djaid  and Konstantin Nikolic   \nObject and significance: the G9a enzyme is an epigenomic regulator, making gene expression directly dependent on how various substances in the cell affect this enzyme. Therefore, it is crucial to consider this impact in any biochemical research involving the development of new compounds introduced into the body. While this can be examined experimentally, it would be highly advantageous to predict theseeffects using computer simulations. Purpose: the purpose of the model was to assist in answering the question of the potential effect that a compound under development could have on the G9a activity, and thus reduce the need for laboratory experiments and facilitate faster and more productive research and development. Solution: the paper proposes a cost-effective machine learning model that determines whether a compound is an active G9a inhibitor. The proposed approach utilises the already existing very extensive PubChem database. The starting point was the quantitative high-throughput screening assay for inhibitors of histone lysine methyltransferase G9a (also available on PubChem) which screened around 350 000 compounds. For these compounds, datasets of 60 features were created. Then different ML algorithms were deployed to find the best performing one, which can then be used to predict if some untested compound would actively inhibit G9a. Results: six different ML classifiers have been implemented on five dataset variations. Different variants of the dataset were created by using two different data balancing approaches and including or not the influence of water solubility at a pH of 7 .4. The most successful combination was a dataset with five features and a random forest classifier that reached 90% accuracy. The classifier was trained with 60 244 and tested with 15 062 compounds. Feature reduction was obtained by analysing three different feature importance algorithms, which resulted in not only feature reduction but also some insights for further biochemical research.  \n1. Introduction  \nThe euchromatic histone–lysine N-methyltransferase (G9a) was discovered around 30 years ago, and along with its epigenetic key role, it has been found that this enzyme is also a coregulator of transcription factors and stero","cbCaiuMH4nk8Subx","https://ap.wps.com/l/cbCaiuMH4nk8Subx","pdf",1012608,1,10,"English","en",105,"# Introduction\n## G9a and its biological significance\n## Machine learning in biochemical research\n## Study motivation and gap in prior work","[{\"question\":\"What problem does the model address?\",\"answer\":\"It predicts the potential effect of a developing compound on G9a activity, specifically whether the compound is an active G9a inhibitor, to reduce laboratory testing needs.\"},{\"question\":\"What data source and initial screening scale are used?\",\"answer\":\"The approach uses the existing PubChem database, based on quantitative high-throughput screening for G9a inhibitors, which screened around 350,000 compounds.\"},{\"question\":\"How is model performance evaluated and what is the best result?\",\"answer\":\"Six classifiers are tested across five dataset variations built with different balancing methods and optional water solubility at pH 7.4. The top-performing setup uses a five-feature dataset with a random forest classifier, reaching about 90% accuracy.\"}]","Application of machine learning for predicting G9a inhibitors | PDF",1785941803,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"application-of-machine-learning-for-predicting-g9a-inhibitors","",{"@graph":36,"@context":86},[37,54,69],{"@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/application-of-machine-learning-for-predicting-g9a-inhibitors/127799/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the model address?","Question",{"text":76,"@type":77},"It predicts the potential effect of a developing compound on G9a activity, specifically whether the compound is an active G9a inhibitor, to reduce laboratory testing needs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data source and initial screening scale are used?",{"text":81,"@type":77},"The approach uses the existing PubChem database, based on quantitative high-throughput screening for G9a inhibitors, which screened around 350,000 compounds.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated and what is the best result?",{"text":85,"@type":77},"Six classifiers are tested across five dataset variations built with different balancing methods and optional water solubility at pH 7.4. 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