{"id":16860,"date":"2026-04-28T09:06:33","date_gmt":"2026-04-28T09:06:33","guid":{"rendered":"https:\/\/www.sygnaturediscovery.com\/?post_type=poster&#038;p=16860"},"modified":"2026-04-30T11:52:12","modified_gmt":"2026-04-30T11:52:12","slug":"ontology-driven-llm-platform-for-assayability-evidence-from-literature","status":"publish","type":"poster","link":"https:\/\/www.sygnaturediscovery.com\/fr\/poster\/ontology-driven-llm-platform-for-assayability-evidence-from-literature\/","title":{"rendered":"Ontology\u2011driven LLM Platform for Assayability Evidence from Literature"},"content":{"rendered":"\n<h2 class=\"wp-block-heading has-text-2-xl-font-size\">An ontology\u2011driven, LLM\u2011enabled platform for extracting and structuring assayability evidence from biomedical literature<\/h2>\n\n\n\n<p>High attrition rates in drug discovery continue to place increasing emphasis on robust early target validation. However, critical assayability evidence, detailing which assays, models and cellular systems can generate decision-enabling data, is often fragmented across biomedical literature and requires time-consuming manual review. <\/p>\n\n\n\n<p>In this poster, we describe an LLM-enabled, ontology-driven platform designed to automatically extract, normalise and prioritize assayability evidence from large-scale literature sources. By transforming unstructured publications into a structured, target-centric resource, the platform helps teams assess target tractability faster and with greater confidence. <\/p>\n\n\n\n<h3 class=\"wp-block-heading has-text-xl-font-size\">What the poster covers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Automated literature mining at scale<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-left:var(--wp--preset--spacing--20)\">Use of transformer-based language model to extract genes, assays, <em>in vivo <\/em>models and cell lines from millions of abstracts<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Fine-tuned biomedical NER<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-left:var(--wp--preset--spacing--20)\">A staged training strategy based on Bioformer-16L, delivering robust entity recognition performance for experimentally relevant concepts that are difficult to capture with rule-based approaches<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ontology-driven normalisation<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-left:var(--wp--preset--spacing--20)\">Introduction of <em>SygOntology<\/em>, an extensible, purpose-built ontology that integrated public resources with curated terms to resolve ambiguous biomedical language and inconsistent nomenclature<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Capability-aware evidence ranking<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-left:var(--wp--preset--spacing--20)\">Target-assay relationships are ranked using literature strength, recency and frequency, combined with internal experimental feasibility, surfacing the most actionable experiments rather than the most commonly reported ones<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>A modular, extensible workflow<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-left:var(--wp--preset--spacing--20)\">A scalable architecture that supports ongoing refinement, human oversight, and future expansion to additional entity types and full-text analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"featured_media":16952,"template":"","category":[693,679,697,678,689],"class_list":["post-16860","poster","type-poster","status-publish","has-post-thumbnail","hentry","category-bioinformatics","category-computer-aided-drug-design","category-generative-ai-and-machine-learning","category-integrated-drug-discovery","category-target-identification-and-validation"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ontology\u2011driven LLM Platform for Assayability Evidence from Literature<\/title>\n<meta name=\"description\" content=\"An ontology\u2011driven, LLM\u2011enabled platform for extracting, normalising and ranking assayability evidence from literature.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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