整合人工智能和手动策划,以增强生物试验注释在ChEMBL中的功能
Ines Smit1, Melissa F Adasme1, Emma Manners1
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB101SD, UK.
Journal of cheminformatics
|February 12, 2026
概括
提高ChEMBL生物测试数据质量对于化学信息学和机器学习 (ML) 至关重要. 最近的努力改善了测试元数据标准化和机器可读性,促进了FAIR数据原则,用于强大的复合目标建模.
科学领域:
- 化学信息学 化学信息学
- 对生物活性数据的分析分析.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 在ChEMBL数据库中,生物活性数据的数量和多样性不断增加.
- 标准化,可互操作和机器可读的测试元数据对于化学信息学和机器学习 (ML) 应用至关重要.
- 目前的ChEMBL生物测试注释需要改进,以提高数据质量和细节性.
研究的目的:
- 介绍最近在ChEMBL数据库中提高生物试验注释的质量和细节性的努力.
- 改进测试元数据的标准化,互操作性和机器可读性.
- 为了实现更强大的下游分析和精确的复合目标活动建模.
主要方法:
- 实现了一个"完美的测试描述"模板,以实现一致的注释.
- 利用自然语言处理 (NLP) 技术和多类分类来自动提取测试参数和分类遗留数据.
- 开发并验证了一个基于spaCy的命名实体识别 (NER) 模型来识别实验方法.
- 创建了一个补充的分类模型来完善ASSAY_TYPE分类.
- 改进了ADME终点,有机体和蛋白质变异注释的元数据提取,以及使用text2term.term等工具进行本体学链接.
主要成果:
- "完美的测试描述"模板指导一致的注释实践.
- NLP和分类模型自动提取关键测试参数,并分配广泛的测试类别.
- 基于spaCy的NER模型在识别实验方法时实现了高精度和回忆.
- 一个补充的分类模型增强了ASSAY_TYPE分类,超出了现有的模式.
- 在元数据提取和本体学链接方面的改进提高了ChEMBL生物试验数据的公平性.
结论:
- 最近的改进显著提高了ChEMBL生物测试注释的质量和细粒度.
- 这些改进推动了ChEMBL生物测试数据的可查找,可访问,可互操作和可重复使用 (FAIR) 原则.
- 增强的数据可以实现更强大的下游分析和更精确的化合物目标活动建模在化学信息学和机器学习应用中.
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