命名实体在科学文献中对药物动力学参数的认可
Ferran Gonzalez Hernandez1, Quang Nguyen2, Victoria C Smith2
1Department of Computer Science, University College London, London, UK. ferran.hernandez.17@ucl.ac.uk.
准确的药物开发依赖于预测药理动力学 (PK) 概况. 这项研究引入了用于改进PK命名实体识别 (NER) 的新型语言模型和体,增强了早期候选药物预测.
科学领域:
- 药理动力学 药理动力学
- 计算生物学 计算生物学
- 药物开发 药物开发
背景情况:
- 药物吸收,分布,新陈代谢和分泌 (ADME) 的准确预测在早期药物开发中至关重要,以减少高失败率.
- 现有的药理动力学 (PK) 数据库往往不完整或过时,需要手动搜索文献以获得PK参数估计.
- 通过文本挖掘自动化PK参数提取是有希望的,但其准确性受到PK术语命名实体识别 (NER) 的限制所阻碍.
研究的目的:
- 通过开发专门的 corpora 和语言模型来解决 PK 命名实体识别 (NER) 的瓶.
- 提高从科学文献中提取药理动力学参数的准确性.
- 促进开发用于PK信息提取的自动化管道,并提高临床前药物开发预测.
主要方法:
- 从PubMed文献中使用主动学习开发了一个新的注释体,包含超过4000个PK实体提及.
- 精细调整和评估各种命名实体识别 (NER) 架构在专业的PK体上.
- 将模型性能与启发式方法和在现有,较少专业化的公司上训练的模型进行比较.
主要成果:
- 微调的BioBERT在PK NER中获得了最高的性能,在识别PK参数提及时,其严格的F1得分为90.37%.
- 开发的模型显著优于现有的启发式方法和在一般公司上训练的模型.
- 开源版本的PK NER模型和注释的语料库旨在加速研究和开发.
结论:
- 新型语言模型和注释体显著改善了对药物动力学参数的命名实体识别 (NER).
- 加强PK NER能力对于在药物开发中推进自动信息提取至关重要.
- 预计开源资源的发布将加速端到端PK预测管道的开发,并改善临床前评估.
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