[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128404-en":3,"doc-seo-128404-105":31,"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":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},128404,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Prodrug-ML - Prodrug-likeness prediction via machine learning on sampled negative decoys","A prodrug is a pharmacologically inactive derivative that undergoes bioreversible transformation in vivo to release an active parent drug, improving solubility, permeability, and targeting. Prodrug-ML addresses the lack of reliable negative examples in in silico prodrug screening that leads to class imbalance and benchmark unreliability. The method prioritizes prodrug-likeness candidates and reduces wet-lab burden by scoring candidate structures, using property-controlled negative cohorts and decoy hardening, and delivering strong discrimination and early retrieval metrics.","University of Birmingham  \nProdrug-ML  \nUgurlu, Sadettin Y. ; He, Shan  \nDOI:  \n10.1007/s10822-025-00725-x  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nUgurlu, SY & He, S 2026, 'Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys', Journal of Computer-Aided Molecular Design, vol. 40, no. 1, 45. [https://doi.org/10.1007/s10822-025-](https://doi.org/10.1007/s10822-025-)[ ](https://doi.org/10.1007/s10822-025-)[00725-x](00725-x)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Feb. 2026  \nProdrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys  \nSadettinY. Ugurlu1,2 · Shan He3  \nReceived: 22 September 2025 / Accepted: 23 November 2025 © The Author(s) 2026  \nAbstract  \nA prodrug is a pharmacologically inactive (or attenuated) derivative that undergoes bioreversible transformation in vivo to release an active parent drug, enabling temporary optimization of properties such as solubility, permeability, and targeting. Despite expanding catalogs of known prodrugs, in silico screening remains limited by the absence of reliable negative examples: training/evaluation sets often contain only positives or ad-hoc decoys, leading to class imbalance, propertymismatch shortcuts, and irreproducible benchmarks. Unfortunately, the limitation of reliable negatives has resulted in there being no efficient machine learning-based prodrug screening approach. Therefore, we introduce Prodrug-ML, an efficient machine learning-based screen for prodrug-likeness that prioritizes candidates rather than asserting mechanistic truth. Prodrug-ML helps medicinal chemists triage prodrugging ideas during hit-to-lead and lead optimization, filter enumerated libraries of promoiety–attachment variants before ADMET assays, and retrospectively mine internal/ChEMBL-like collections to surface likely prodrug chemotypes. In practice, users (i) generate or collect candidate structures (e.g., parent drug ± pro-moieties), (ii) score them with Prodrug-ML, and (iii) advance only high-scoring candidates to synthesis/assay, thereby reducing wet-lab load while maintaining chemical diversity. In order to achieve such practical usage, the ProdrugML framework, containing the default classifier, LightGBM, addresses these issues by (i) constructing three complementary, property-controlled negative cohorts (DUD-E–style near-misses, random ChEMBL, and stric","cbCaim2xvniTrGu9","https://ap.wps.com/l/cbCaim2xvniTrGu9","pdf",8954742,3,1,47,"English","en",105,"# Abstract\n## Prodrug concept and screening challenge\n## Prodrug-ML approach and workflow\n## Negative cohort construction and safeguards\n## Evaluation protocol and benchmark results\n# Introduction\n## Origins of the prodrug concept","[{\"question\":\"What problem does Prodrug-ML target in prodrug screening?\",\"answer\":\"In silico screening is limited by unreliable negative examples, which creates class imbalance, property-mismatch shortcuts, and irreproducible benchmarks. Prodrug-ML focuses on ranking prodrug-likeness candidates despite this constraint.\"},{\"question\":\"How does Prodrug-ML generate training and evaluation negatives?\",\"answer\":\"It constructs three complementary, property-controlled negative cohorts, including DUD-E–style near-misses, random ChEMBL samples, and strictly filtered ChEMBL. It also applies hardness control and label-noise guardrails for decoys.\"},{\"question\":\"How does Prodrug-ML help medicinal chemists in practice?\",\"answer\":\"Users generate or collect candidate structures, score them with Prodrug-ML, and advance only high-scoring candidates to synthesis and assay. This reduces wet-lab load while maintaining chemical diversity during hit-to-lead and lead optimization.\"}]","Prodrug-ML - Prodrug-likeness prediction via machine learning on sampled negative decoys | PDF",1785947326,118,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prodrug-ml-prodrug-likeness-prediction-via-machine-learning-on-sampled-negative-decoys","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/prodrug-ml-prodrug-likeness-prediction-via-machine-learning-on-sampled-negative-decoys/128404/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","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 Prodrug-ML target in prodrug screening?","Question",{"text":76,"@type":77},"In silico screening is limited by unreliable negative examples, which creates class imbalance, property-mismatch shortcuts, and irreproducible benchmarks. 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