[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31095":3,"doc-seo-31095":21},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"update_tm":20},31095,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Gene Expression, Docking and Machine Learning in Malaria Drug Discovery: A Systematic Review","Malaria remains a major global health threat as resistance undermines current therapies, driving the search for novel antimalarial targets and compounds. This PRISMA-guided systematic review synthesizes studies from 2014 to 2024 on integrating gene expression profiling, molecular docking, and machine learning for Plasmodium biology. Findings show molecular docking as the leading approach, complemented by in vitro assays, ADMET profiling, and RNA-seq evidence from herbal treatments. Machine-learning models such as random forest and SVM demonstrate strong predictive performance for bioactivity and resistance patterns. Multiomics integration improves prioritization of candidate compounds and targets, while limitations in in vivo evidence and inconsistent methodologies constrain clinical translation.","cbCaiqVCYQmbeeLi","https://ap.wps.com/l/cbCaiqVCYQmbeeLi","pdf",1747076,1,1778619656,{"code":4,"msg":22,"data":23},"ok",{"site_id":24,"language":25,"slug":26,"title":13,"keywords":27,"description":14,"schema_data":28,"social_meta":62,"head_meta":64,"extra_data":66,"updated_unix":20},105,"en","gene-expression-docking-and-machine-learning-in-malaria-drug-discovery-a-systematic-review","",{"@graph":29,"@context":61},[30,47],{"@type":31,"itemListElement":32},"BreadcrumbList",[33,37,41,44],{"item":34,"name":35,"@type":36,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":38,"name":39,"@type":36,"position":40},"https://docshare.wps.com/document/","Document",2,{"item":42,"name":12,"@type":36,"position":43},"https://docshare.wps.com/document/research-report/",3,{"item":45,"name":13,"@type":36,"position":46},"https://docshare.wps.com/document/gene-expression-docking-and-machine-learning-in-malaria-drug-discovery-a-systematic-review/31095",4,{"url":45,"name":13,"@type":48,"author":49,"headline":13,"publisher":51,"fileFormat":54,"description":14,"dateModified":55,"datePublished":55,"encodingFormat":54,"isAccessibleForFree":56,"interactionStatistic":57},"DigitalDocument",{"name":9,"@type":50},"Person",{"url":34,"name":52,"@type":53},"DocShare","Organization","application/pdf","2026-05-12",true,{"@type":58,"interactionType":59,"userInteractionCount":4},"InteractionCounter",{"@type":60},"ViewAction","https://schema.org",{"og:url":45,"og:type":63,"og:title":13,"og:site_name":52,"og:description":14},"article",{"robots":65,"canonical":45},"index,follow",{"doc_id":7,"site_id":24}]