[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125852-en":3,"doc-seo-125852-105":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":11,"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},125852,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Best practices for machine learning in antibody discovery and development - perspective review and evaluation guidelines","Therapeutic antibody discovery and development has become standard for treating disease, yet increasingly sophisticated constructs such as multispecifics make conventional optimization inefficient. Machine learning offers an in silico route that can reduce the number of experiments, lowering cost and accelerating drug-product development. However, diverse datasets and inconsistent evaluation metrics hinder comparison and expert assessment, limiting industry adoption. This perspective reviews current practices, identifies pitfalls, and proposes end-to-end method development and evaluation guidelines for ML in therapeutic antibody R&D.","Best practices for machine learning in antibody discovery and development  \nLeonard Wossnig1,2, ✝, Norbert Furtmann3, Andrew Buchanan4, Sandeep Kumar5, and Victor Greiff6  \n1 LabGenius Ltd., The Biscuit Factory, 100 Drummond Road, London, SE16 4DG London, United Kingdom; Orcid: 0000-0002-0861-9540  \n2 Department of Computer Science, University College London, 66-72 Gower St, WC1E 6EA London, United Kingdom  \n3 R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany; Orcid: 0009-0006-8226-7586  \n4 Biologics Engineering, R&D, AstraZeneca, Cambridge, CB2 0AA , United Kingdom; Orcid: 0000-0002-5191-7682  \n5 Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, United States of America; Orcid: 0000-0003-2840-6398  \n6 Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway; Orcid: 0000-0003-2622-5032  \n✝ Corresponding author  \nAbstract:  \nOver the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning ( ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost.  \nOver the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development ( D&D) . However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall  \nprogress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups.  \nTo address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly defi ne a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody D&D. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage.  \nFigure 1: Overview of the entire machine learning ( ML) process for antibody R&D from data collection to model evaluation.  \n1. Introduction  \nThe development of antibody-based drugs has revolutionised the field of medicine, providing effective treatments for a wide range of diseases [1,2] . However, although the drug discovery process has proven effective, it is complex, expensive, time-consuming, and has room for improvement [3–7] . Recent advances in ML have the potential to accelerate and opti mise this process by enabling the identification of better biotherapeutic drug candidates more rapidly, and thereby reducing the cost and timelines for antibody drug discovery [8–10] .  \nThe rapidly evolving landscape of ML-guided therapeutic antibody research and development ( R&D) holds immense potential for the biopharmaceutical industry. Although initial success stories have emerged, its ‘ real world’ impact is thus far relatively minimal [11] . To unlock the full potential and demonstrate significant impact in commercial drug discovery and development settings, it is essential to establish standardised guidelines and best practices for applications of ML at every step of bio logic drug discovery and development projects. These include in-silico design of antibody candidate drugs; computational identification or design of high affinity specific function relevant epitopes; accurate pr","cbCailSHqrd7y2zP","https://ap.wps.com/l/cbCailSHqrd7y2zP","pdf",2649692,1,46,"English","en",105,"# Introduction\n## ML-guided therapeutic antibody R&D overview\n## Need for standards, benchmarks, and best practices\n# ML process for antibody R&D\n## Data collection to model evaluation\n# Current practices and common pitfalls\n## Method development and evaluation guidelines","[{\"question\":\"Why are conventional optimization approaches becoming less effective for therapeutic antibody development?\",\"answer\":\"As therapeutic antibody constructs become more sophisticated, such as multispecifics, traditional optimization methods become increasingly inefficient.\"},{\"question\":\"What is the main limitation of existing ML-guided antibody discovery literature?\",\"answer\":\"Results are difficult to compare or assess due to high diversity in datasets and differences in evaluation techniques, metrics, and benchmarking.\"},{\"question\":\"What does the perspective aim to deliver for ML-based therapeutic antibody R\\u0026D?\",\"answer\":\"It critically reviews current practices, explains common pitfalls, and defines end-to-end method development and evaluation guidelines that can be applied across different ML-based techniques.\"}]","Best practices for machine learning in antibody discovery and development - perspective review and evaluation guidelines | PDF",1785901587,116,{"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},"best-practices-for-machine-learning-in-antibody-discovery-and-development-perspective-review-and-evaluation-guidelines","",{"@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/best-practices-for-machine-learning-in-antibody-discovery-and-development-perspective-review-and-evaluation-guidelines/125852/",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","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are conventional optimization approaches becoming less effective for therapeutic antibody development?","Question",{"text":76,"@type":77},"As therapeutic antibody constructs become more sophisticated, such as multispecifics, traditional optimization methods become increasingly inefficient.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the main limitation of existing ML-guided antibody discovery literature?",{"text":81,"@type":77},"Results are difficult to compare or assess due to high diversity in datasets and differences in evaluation techniques, metrics, and benchmarking.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the perspective aim to deliver for ML-based therapeutic antibody R&D?",{"text":85,"@type":77},"It critically reviews current practices, explains common pitfalls, and defines end-to-end method development and evaluation guidelines that can be applied across different ML-based techniques.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]