基准测试 In Silico 代谢物预测工具与人类放射标记的ADME数据对比,用于小分子药物
Lei Gao1,2, Shu Yan1, Kaiyuan Feng1,3
1Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201210, China.
Journal of chemical information and modeling
|February 12, 2026
概括
预测药物代谢物的人工智能 (AI) 工具显示出希望,但还不能取代实验研究. 目前的AI模型提供了对人类新陈代谢的部分见解,有助于早期药物开发查.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 药物开发 药物开发
背景情况:
- 人工智能 (AI) 驱动的*in silico*代谢物预测对于现代药物开发至关重要.
- 评估这些人工智能工具与高质量的人类数据的准确性至关重要,但仍然具有挑战性.
研究的目的:
- 评估四种开放式人工智能代谢物预测模型 (SyGMa,GLORYx,BioTransformer 3.0,MetaPredictor,MetaTrans) 的性能.
- 通过使用既定的性能指标,将AI预测的代谢物与实验确定的人类代谢物进行比较.
主要方法:
- 利用11种小分子药物的已发表的人类放射性标记的吸收,分布,新陈代谢和分泌 (ADME) 数据.
- 通过回忆,精度,平衡精度,F1和Jaccard分数来评估模型性能.
- 将预测的代谢物与实验确定的人类代谢物进行比较.
主要成果:
- 代谢覆盖和预测准确性之间存在一个权衡.
- 生物转换器3.0展示了最好的整体性能,平衡覆盖范围和相关性.
- 没有模型预测了代谢物丰度,也没有捕获了mercapturic acid路径.
结论:
- 目前的AI代谢物预测工具提供部分洞察力,最适合用于早期药物开发查.
- 为了提高准确性,人工智能的进一步进步,包括分子表示和途径完整性,是必要的.
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