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BioMiner system automates protein-ligand bioactivity extraction from literature

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BioMiner, a novel multi-modal system designed to automatically extract protein-ligand bioactivity data from scientific literature. This system addresses the challenge of manual curation by interpreting semantic information from text, tables, and figures, and reconstructing complex chemical structures. BioMiner achieves an F1 score of 0.32 for bioactivity triplets and has demonstrated practical utility in building pre-training databases, improving quantitative structure-activity relationship (QSAR) models, and accelerating bioactivity annotation. AI

IMPACT Automates extraction of critical bioactivity data from literature, accelerating drug discovery and improving downstream model performance.

RANK_REASON This is a research paper describing a new system and benchmark for automated data extraction from scientific literature.

Read on arXiv cs.AI →

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

BioMiner system automates protein-ligand bioactivity extraction from literature

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This is a research paper describing a new system and benchmark for automated data extraction from scientific literature.
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Enhong Chen ·

    BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

    Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical sem…

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