[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-331827-105":59,"doc-detail-331827-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","harnessing-machine-learning-to-find-synergistic-combinations-for-fda-approved-cancer-drugs","Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs","","Combination therapy underpins modern cancer chemotherapy by using multiple anti-cancer agents to enhance efficacy and counter multidrug resistance, yet interactions can be synergistic, additive, or antagonistic. The study proposes a machine-learning framework that collects and annotates data from the O’Neil drug interaction dataset, preprocesses inputs, and trains classification models to label combination types. Regression models estimate combination sensitivity scores, while feature and mechanism-of-action analysis explains synergy patterns. Predicted high-likelihood pairs highlight kinase-mTOR, DNA-damage, and HDAC-related benefits across multiple tumor types, with Gemcitabine, MK-8776, and AZD1775 recurring across cancers.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/harnessing-machine-learning-to-find-synergistic-combinations-for-fda-approved-cancer-drugs/331827/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/harnessing-machine-learning-to-find-synergistic-combinations-for-fda-approved-cancer-drugs/331827.png","ImageObject",300,407,{"name":92,"@type":93},"Logic","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in cancer combination therapy?","Question",{"text":112,"@type":113},"The study addresses the difficulty of identifying drug pairs whose interactions are synergistic, since combinations can show synergy, additivity, or antagonism and can be limited by monotherapy resistance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed framework predict synergistic drug combinations?",{"text":117,"@type":113},"It collects and annotates data from the O’Neil drug interaction dataset, preprocesses it, splits data into training and test sets, then trains classification models to label combination types and regression models to predict sensitivity scores.",{"name":119,"@type":110,"acceptedAnswer":120},"Which types of drug pairings and drugs are highlighted as frequently synergistic?",{"text":121,"@type":113},"The study reports benefit for combinations such as kinase inhibitors with mTOR inhibitors and with DNA damage-inducing or HDAC inhibitors, and it highlights Gemcitabine, MK-8776, and AZD1775 as frequently synergizing across cancer types.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},331827,1790581018,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nHarnessing machine learning to find synergistic combinations for FDA‑approved cancer drugs  \nTarek Abd El‑Hafeez1,2*, Mahmoud Y. Shams 3,6, Yaseen A. M. M. Elshaier 5, Heba Mamdouh Farghaly1 & Aboul Ella Hassanien4,6*  \nCombination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti‑cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage‑inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK‑8776 andAZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi‑drug regimens.  \nAbbreviations  \nCSS Combination sensitivity scores DNA Deoxyribonucleic acid  \nFDA Food and Drug Administration GCN Graph convolutional network HDAC Histone deacetylase  \nIQR Inter-quartile range KNN K-nearest neighbors LR Logistic regression  \nML Machine learning  \nMAE Mean absolute error  \nMSE Mean squared error NB Naive Bayes  \nRF Random forest  \nROCS Rapid overlay chemical similarity R2 (R-squared) coefficient of determination  \nAs research into cancer cell abnormalities continues, an increasing number of anti-cancer medications are being developed and assessed. However, the efficacy of one medication or a single target drug as a monotherapy is limited due to innate or acquired resistance. To address this challenge, a more effective approach is drug combination treatment. The use of drug combinations has proven to be an effective strategy for treating diseases that are challenging to manage, including cancer and infectious infections1–4. The use of drug combination therapy  \n1Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt. 2Computer Science Unit, Deraya University, El-Minia, Egypt. 3Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt. 4Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt. 5Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt. 6Scientific  \nResearch Group in Egypt (SRGE), Cairo, Egypt.*[email: tarek@mu.edu.eg](email: tarek@mu.edu.eg); [aboitcairo@gmail.com](aboitcairo@gmail.com)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \ncan inhibit multiple targets, thereby overcoming drug resistance in infectious fungal diseases5–7. The explanation for this is that biological systems are less capable of correcting for the action of two or more medications at the same time2,6,8,9. Effective medication combinations have traditionally been established by evaluating all potential combinations of a pre-defined set of pharmaceuticals in an experimental setting6, 10. The screening of drug combinations is a challenging task due to the vast number of available drugs, which makes the process time-consuming, labor-intensive,","cbCaigmrjzznsITv","https://ap.wps.com/l/cbCaigmrjzznsITv","pdf",1690260,23,"English","# Abstract\n## Combination therapy rationale\n## Machine learning framework pipeline\n## Classification and regression modeling\n## Feature and mechanism analysis\n## Key synergistic findings","[{\"question\":\"What problem does the study address in cancer combination therapy?\",\"answer\":\"The study addresses the difficulty of identifying drug pairs whose interactions are synergistic, since combinations can show synergy, additivity, or antagonism and can be limited by monotherapy resistance.\"},{\"question\":\"How does the proposed framework predict synergistic drug combinations?\",\"answer\":\"It collects and annotates data from the O’Neil drug interaction dataset, preprocesses it, splits data into training and test sets, then trains classification models to label combination types and regression models to predict sensitivity scores.\"},{\"question\":\"Which types of drug pairings and drugs are highlighted as frequently synergistic?\",\"answer\":\"The study reports benefit for combinations such as kinase inhibitors with mTOR inhibitors and with DNA damage-inducing or HDAC inhibitors, and it highlights Gemcitabine, MK-8776, and AZD1775 as frequently synergizing across cancer types.\"}]","Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs | PDF",1789995366,58]