[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126135-en":3,"doc-seo-126135-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126135,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Machine Learning Algorithms in Drug Screening","Drug screening in medicine often relies on labor-intensive in vivo experiments that become impractical when evaluating thousands of compounds individually. The study proposes a computer-based preliminary screening approach using machine learning to relate compound molecular structures to ERα activity. Focusing on breast-cancer relevance to ERα, it builds a molecular-structure prediction model with random forest regression, then refines quantitative assessment using comparative kernel functions and support vector regression with a radial basis kernel. The method helps narrow experimental scope and supports more accurate drug optimization.","Application of machine learning algorithms in drug screening  \nKe Jin1, Cunqing Rong2, Jincai Chang3  \n1, 2College of Sciences, North China University of Science and Technology, Tangshan, 063210, China  \n33D Modeling and Application Innovation Laboratory, North China University of Science and Technology, Tangshan, 063210, China  \n3Corresponding author  \n[E-mail:](E-mail:1 872521525@qq.com)[1](E-mail:1 872521525@qq.com)[ 872521525@qq.com](E-mail:1 872521525@qq.com), [2](2 1919405316@qq.com)[ 1919405316@qq.com](2 1919405316@qq.com), [3](3jincai@nest.edu.cn)[jincai@nest.edu.cn](3jincai@nest.edu.cn)  \nReceived 27 March 2023; accepted 19 September 2023; published online 4 November 2023 DOI [https://doi.org/10.21595/chs.2023.23292](https://doi.org/10.21595/chs.2023.23292)  \nCopyright © 2023 Ke Jin, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract. At present, in the medical field, drug screening is usually performed using in vivo drug experiments. However, it is very time-consuming and laborious to conduct in vivo experiments on a large number of drugs to be screened one by one. This paper attempts to propose using machine learning algorithms to perform preliminary screening of a large number of compounds to be screened and their molecular structures to reduce the workload of in vivo experiments. Among them, it is internationally recognized that there is an important association between breast cancer progression and the alpha subtype of the estrogen receptor. Anti-breast cancer drug candidates with excellent efficacy need to contain compounds that can better antagonize ERα activity. In this paper, the research object is narrowed down from compounds to the molecular structure of the compounds, and then the random forest regression algorithm is used to develop the molecular structure-ERα activity prediction model. Molecular structures with significant effects on biological activity were screened from molecular structure descriptors in numerous compounds. Four different kernel functions were used to conduct comparative experiments, and finally a support vector regression algorithm based on radial basis kernel function was established, which realized the quantitative prediction of compounds on biological activity of ERα, and could find potential compounds beneficial to breast cancer treatment. This is a novel, computer-based method for preliminary drug screening, which can help medical researchers effectively narrow the scope of experiments and achieve more accurate optimization of drugs.  \nKeywords: drug screening, random forest regression, radial basis kernel function, support vector machine.  \n1. Introduction  \nIn recent years, with the accelerated pace of people’s lives, external factors such as smoking, alcoholism and poor eating habits lead to an increasing risk of breast cancer. Breast cancer is known as the number one killer that threatens women’s health. According to the “Global Cancer Statistics 2020” jointly released by the World Health Organization (WHO), the International Agency for Research on Cancer (IARC) and the Cancer Society (ACS) in 2020, the incidence of breast cancer is increasing rapidly, accounting for 11.7 % of all cancer cases. Breast cancer, with 2.26 million cases worldwide, has surpassed lung cancer with 2.2 million cases to become the number one cancer in the world, and the number of cases of breast cancer is higher in developing countries [1]. Anticancer treatment has always been an important problem in medicine, but good development and progress have been made in the detection and precise treatment of breast cancer [2]. If timely detection and active treatment are available, most breast cancer patients have a higher probability of survival than other cancer patients. Under the current level of medical care, the 5-year surviv","cbCaieohQzuEc4ZC","https://ap.wps.com/l/cbCaieohQzuEc4ZC","pdf",1384098,5,1,14,"English","en",105,"# Introduction\n## Breast cancer risk and the need for efficient drug screening\n## ERα as a therapeutic target\n## Limitations of in vivo screening and motivation for ML models","[{\"question\":\"Why is machine learning proposed for drug screening in this research?\",\"answer\":\"In vivo screening is time-consuming and labor-intensive when many compounds must be evaluated. Machine learning enables preliminary screening by predicting biological activity from molecular structures.\"},{\"question\":\"What is the main biological activity target used in the model?\",\"answer\":\"The model predicts ERα (estrogen receptor alpha) activity, which is closely associated with breast cancer progression and therapy response.\"},{\"question\":\"Which machine learning methods are used to build and evaluate predictions?\",\"answer\":\"Random forest regression is used to develop the molecular-structure to ERα activity prediction model, and support vector regression with a radial basis kernel function is established after comparative experiments with different kernel functions.\"}]","Application of Machine Learning Algorithms in Drug Screening | PDF",1785903345,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"application-of-machine-learning-algorithms-in-drug-screening","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/application-of-machine-learning-algorithms-in-drug-screening/126135/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is machine learning proposed for drug screening in this research?","Question",{"text":77,"@type":78},"In vivo screening is time-consuming and labor-intensive when many compounds must be evaluated. Machine learning enables preliminary screening by predicting biological activity from molecular structures.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the main biological activity target used in the model?",{"text":82,"@type":78},"The model predicts ERα (estrogen receptor alpha) activity, which is closely associated with breast cancer progression and therapy response.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning methods are used to build and evaluate predictions?",{"text":86,"@type":78},"Random forest regression is used to develop the molecular-structure to ERα activity prediction model, and support vector regression with a radial basis kernel function is established after comparative experiments with different kernel functions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]