相关实验视频
Updated: Sep 12, 2025
14:18
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
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代谢部位预测与 Aleatoric 和 Epistemic 不确定性量化量化
Roxane Axel Jacob1,2,3, Oliver Wieder1,2, Ya Chen1
1Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, Vienna 1090, Austria.
Journal of chemical information and modeling
|August 6, 2025
概括
这项研究介绍了aweSOM,这是一个图形神经网络模型,用于预测药物代谢部位. 它量化了预测不确定性,对于可靠的药物设计和开发至关重要.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 在 silico 代谢预测优化了异生菌性质,同时保持了生物活性.
- 代谢部位 (SOM) 模型识别了药物分子中代谢不稳定的原子位置.
研究的目的:
- 介绍 aweSOM,一种基于图形神经网络 (GNN) 的新型 SOM 预测模型.
- 为了利用深层组合来建模和分割预测不确定性成 aleatoric 和 epistemic 组件.
主要方法:
- 开发一个基于GNN的模型,命名为awesom.
- 应用深度组合技术来量化预测不确定性.
- 在高质量的数据集上对不确定性估计的全面评估.
主要成果:
- aweSOM有效地模拟了总预测准确度及其不确定性组成部分.
- 评估确定了限制当前SOM预测模型性能的主要挑战.
- 该研究提供了改善新陈代谢预测准确性和可靠性的见解.
结论:
- 准确的不确定性估计对于SOM预测模型的实际实用性至关重要.
- 解决已识别的挑战可以推动 in silico代谢预测领域的发展.
- aweSOM为可靠的药物代谢部位识别提供了一个强大的框架.
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