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一种综合性文本挖掘方法,用于发现药理学效应,药物组合,以及重新利用ACE抑制剂的机会
Nadezhda Yu Biziukova1, Polina I Savosina1, Dmitry S Druzhilovskiy1
1Institute of Biomedical Chemistry, 10, Pogodinskaya Street, 119121 Moscow, Russia.
International journal of molecular sciences
|February 27, 2026
概括
本研究引入了一个文本挖掘框架,自动从生物医学文献中提取和结构化关于小分子药物活动的数据. 它揭示了血管酶转化酶 (ACE) 抑制剂的新疗法用途和机制,有助于药物的重新用途.
科学领域:
- 生物医学信息学 生物医学信息学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 生物医学文献包含了关于小分子疗法的大量数据,但系统地提取知识是具有挑战性的.
- 整合有关药物效应,机制和重新利用潜力的信息需要先进的方法.
研究的目的:
- 开发一个集成的文本挖掘框架,用于自动提取和结构化表现低分子量化合物的生物活动.
- 举例来说明使用血管酶转化酶 (ACE) 抑制剂的框架.
主要方法:
- 汇集了超过20,000个PubMed标题/摘要的ACE抑制剂的集体.
- 用于化学化合物,蛋白质/基因和疾病的命名实体识别 (NER).
- 通过PubChem,UniProt和人类疾病本体学采用基于规则的句子级联想提取和规范化实体.
主要成果:
- 提取了大约22000个独特的,正常化的关联 (药物标,药物疾病,药物药物).
- 已确认已知的治疗效果和药物组合.
- 鉴定了ACE抑制剂 (例如,抗瘤,抗纤维素,神经精神疾病) 的未充分研究的活性和新的机制联系.
结论:
- 自动化文献挖掘可以显著推进药理学中的系统知识整合.
- 该框架支持基于数据的假设生成,用于药物的重新用途和安全性评估.
- 强调了超出心血管应用范围的ACE抑制剂的潜力.
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