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AI workflow extracts protein degradation data from scientific papers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an expert-in-the-loop large language model workflow to extract structured assay data from scientific publications, particularly in the field of targeted protein degradation (TPD). This method, demonstrated with limited expert annotations, achieved high accuracy and significantly increased the number of records in TPD databases compared to existing datasets. The resulting database, AutoTPD+, has shown to improve the performance of predictive models for protein degradation activity. AI

IMPACT Accelerates drug discovery by enabling more efficient data extraction and model training in TPD research.

RANK_REASON This is a research paper detailing a new methodology and database for scientific data extraction using AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

AI workflow extracts protein degradation data from scientific papers

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This is a research paper detailing a new methodology and database for scientific data extraction using AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yaochen Rao, Farzaneh Jalalypour, N. M. Anoop Krishnan, Roc\'io Mercado ·

    Agentic Literature Extraction Augments Targeted Protein Degradation Databases

    arXiv:2605.11221v2 Announce Type: replace-cross Abstract: Predictive models in medicinal chemistry depend on structured assay data locked in primary publications. This bottleneck is acute in targeted protein degradation (TPD), where each assay record must combine compound identit…

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