[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81887-en":3,"doc-seo-81887-105":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},81887,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine (OpenDDE)","Accurate biomolecular interaction modeling is a core bottleneck in biology and therapeutic discovery. Open Drug Discovery Engine (OpenDDE) presents an open-source, all-atom foundation model that uses co-folding as an entry point to a scalable AI drug-discovery system. OpenDDE treats structure prediction as a reusable structural reasoning layer spanning sequence–structure–function modeling for complexes, supporting de novo design, affinity estimation, and structure-conditioned optimization. It integrates all-atom architecture advances, atomic latent reasoning, efficient inference, and large-scale data processing, achieving IsoDDE-level co-folding accuracy. Two co-folding scaling-law directions guide continued improvements via data, model, inference, and training scaling, with released code, pipelines, checkpoints, and benchmarks to accelerate community collaboration and enable next-generation therapeutic candidate design.","AUR EKA AI RESEARCH  \narXiv :2607 .03787v 1 [ cs .AI] 4 Jul 2026  \nFolding, Reasoning, and Scaling with Open-source Drug Discovery Engine  \nOpenDDE Project, Aureka AI Research ∗  \nAbstract  \nAccurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery. Here, we introduce Open Drug Discovery Engine (OpenDDE), an open-source, all-atom biomolecular foundation model that uses co-folding as the entry point to a scalable AI-driven drug discovery engine. Rather than treating structure prediction as an isolated endpoint, OpenDDE is designed as a shared structural reasoning layer for modeling sequence–structure–function relationships across biomolecular complexes, enabling complex structure prediction today while providing a foundation for de novo design, affinity estimation, structure-conditioned optimization, and more . OpenDDE integrates advances in all-atom architecture, atomic latent reasoning, inference optimization, and large-scale data processing to achieve IsoDDE-level co-folding accuracy within a reproducible and openly accessible framework. We also identify two scaling-law directions for co-folding models, revealing practical routes for continued improvement through data, model, inference, and training scaling. By releasing training code, inference pipelines, checkpoints, and benchmarks, OpenDDE aims to democratize access to frontier biomolecular intelligence, accelerate global collaboration, and lay an open foundation for next-generation drug discovery systems that can move from predicting molecular structures toward designing, scoring, and optimizing therapeutic candidates for human health.  \n§ GitHub: [https://github.com/aurekaresearch/OpenDDE](https://github.com/aurekaresearch/OpenDDE)  \nõ HuggingFace: [https://huggingface.co/aurekaresearch/OpenDDE](https://huggingface.co/aurekaresearch/OpenDDE)  \nTarget  \nDiscovery & Validation  \nStructure Prediction  \nEnsemble Sampling  \nEpitope Modeling  \nDe novo  \nDesign  \nHit2Lead Optimization  \nBinding Affinity  \nVirtual Cell  \n∗ Full author list in Contributions  \nAUR EKA AI RESEARCH  \nOverview  \nKey Contributions  \n1. Atomic latent reasoning over biomolecular tokens. We incorporate latent reasoning into biomolecular modelling, enabling the model to refine representations of local geometry, chemical context, and cross-molecular interfaces before all-atom structure generation.  \n2. A folding-centered foundation for an extensible drug-discovery engine. OpenDDE currently focuses on structure prediction, but its unified architecture is designed to support future de novo molecular design, affinity prediction, and other structure-conditioned modules.  \n3. Scaling laws and data distillation. We study scaling directions along model-parameter and data axes, and revise distillation strategies for monomeric and multimeric structures to improve robustness, generalization, and training efficiency.  \nCharacterising biomolecular interactions among proteins, nucleic acids, small-molecule ligands and other cellular components is fundamental to understanding biological mechanisms and to modulating disease-relevant processes through therapeutic intervention [1 , 2] . In silico models that can predict these interactions with experimental-level accuracy would therefore provide a structural basis for scalable and reliable drug discovery.  \nAlphaFold2 [3] transformed protein structure prediction by achieving near-experimental accuracy for many protein monomers . AlphaFold3 [4] extended this paradigm to joint, atomic-level modelling of complexes containing proteins, nucleic acids, small molecules, ions and modified residues. More broadly, AlphaFold-style and structure-conditioned models have become reusable engines for epitope prediction, binder design [5–7], virtual screening [8 , 9], conformational sampling [10 , 11] and the acceleration or interpretation of experimental workflows [12] . The recent introduction of IsoDDE further shows that co-folding can se","cbCailVYSjd1ezUc","https://ap.wps.com/l/cbCailVYSjd1ezUc","pdf",9045283,7,1,25,"English","en",105,"# Abstract\n# Target\n## Discovery & Validation\n## Structure Prediction\n## Ensemble Sampling\n## Epitope Modeling\n# Overview\n## Key Contributions\n# Performance\n## Structure Prediction Result","[{\"question\":\"What is OpenDDE, and what role does co-folding play in it?\",\"answer\":\"OpenDDE is an open-source, all-atom generative foundation model for drug discovery. It uses co-folding as the entry point to build a scalable AI-driven engine and a shared structural reasoning layer for modeling sequence–structure–function relationships across biomolecular complexes.\"},{\"question\":\"Which major advances does OpenDDE introduce compared with prior co-folding approaches?\",\"answer\":\"OpenDDE incorporates atomic latent reasoning over biomolecular tokens, uses a folding-centered foundation designed to be extensible beyond structure prediction, and studies scaling-law directions including data distillation strategies for monomeric and multimeric structures.\"},{\"question\":\"How does OpenDDE support scalability and community adoption?\",\"answer\":\"The work identifies two scaling-law directions for co-folding models to guide improvements through data, model, inference, and training scaling. It releases training code, inference pipelines, checkpoints, and benchmarks under an open license to enable reproducibility, validation, and community-driven extension.\"}]","Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine (OpenDDE) | PDF",1784176882,63,{"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},"folding-reasoning-and-scaling-with-open-source-drug-discovery-engine-opendde","",{"@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/folding-reasoning-and-scaling-with-open-source-drug-discovery-engine-opendde/81887/",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","2026-08-01","2026-07-16",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},"What is OpenDDE, and what role does co-folding play in it?","Question",{"text":77,"@type":78},"OpenDDE is an open-source, all-atom generative foundation model for drug discovery. It uses co-folding as the entry point to build a scalable AI-driven engine and a shared structural reasoning layer for modeling sequence–structure–function relationships across biomolecular complexes.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which major advances does OpenDDE introduce compared with prior co-folding approaches?",{"text":82,"@type":78},"OpenDDE incorporates atomic latent reasoning over biomolecular tokens, uses a folding-centered foundation designed to be extensible beyond structure prediction, and studies scaling-law directions including data distillation strategies for monomeric and multimeric structures.",{"name":84,"@type":75,"acceptedAnswer":85},"How does OpenDDE support scalability and community adoption?",{"text":86,"@type":78},"The work identifies two scaling-law directions for co-folding models to guide improvements through data, model, inference, and training scaling. It releases training code, inference pipelines, checkpoints, and benchmarks under an open license to enable reproducibility, validation, and community-driven extension.","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},[95,99,103,107,112,117,121,124,129,132,136],{"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":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"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":108,"slug":139},19,"General","general"]