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AI platform converts drug development documents into knowledge graph

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel agentic-AI platform designed to transform fragmented technical documents into a queryable, dual-layer knowledge graph for CMC process development. This system addresses the challenge of managing vast amounts of information typically scattered across various formats in drug discovery and manufacturing. The platform includes a base layer for lexical information and an intelligence layer for conceptual bridging, with LLM agents selecting optimal retrieval paths for user queries. An evaluation using a Sanofi small-molecule program demonstrated high accuracy in retrieving information, though a stricter assessment revealed areas for improvement in handling comparative and corpus-wide questions. AI

IMPACT This system could streamline knowledge management and accelerate regulatory processes in the pharmaceutical industry.

RANK_REASON The item is an academic paper detailing a new AI system and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI platform converts drug development documents into knowledge graph

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3 / 100
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The item is an academic paper detailing a new AI system and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Yasser Jangjou ·

    From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development

    Chemistry, Manufacturing and Controls (CMC) process development generates an enormous body of technical information across a multi-stage, knowledge-intensive continuum from drug discovery to commercial manufacturing. This knowledge is traditionally fragmented across functions and…

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