GeoDILI:一个强大的和可解释的模型,用于药物诱导的肝损伤预测,使用基于图形神经网络的分子几何表示
Wenxuan Wu1, Jiayu Qian2, Changjie Liang1
1Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Chemical research in toxicology
|October 15, 2023
概括
GeoDILI使用分子几何学准确预测药物诱导的肝损伤 (DILI). 这种可解释图形神经网络的性能优于现有模型,有助于开发更安全的药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 毒理学 毒理学 毒理学
背景情况:
- 药物诱导性肝损伤 (DILI) 是药物开发中的一个主要挑战,往往导致药物戒断.
- 目前的DILI预测模型面临着由于数据集多样化和缺乏可解释性的局限性,经常忽视分子几何学.
研究的目的:
- 开发一个可解释的图形神经网络 (GNN) 模型,GeoDILI,用于准确预测DILI.
- 为了更好地理解肝毒性,将分子几何特性纳入并增强模型的解释性.
主要方法:
- 开发了GeoDILI,一个可解释的GNN,利用基于几何学的预训练分子表示,优化了DILI数据.
- 采用梯度信息,用于高精度的原子级重量和亚结构识别.
- 在标准化数据集上对现有的DILI预测模型,GNN和基于指纹的方法进行基准测试.
主要成果:
- 与现有的DILI预测模型相比,GeoDILI表现出优越的预测性能.
- 该模型成功地确定了与DILI相关的七个精确的,机械上阐明的结构性警报.
- 由于GeoDILI的可解释性,可以扣除导致肝毒性的主导子结构.
结论:
- GeoDILI为预测药物诱导的肝损伤提供了一种强大且可解释的方法.
- 该模型显示了提高药物发现和安全性评估过程的巨大潜力.
- 数据和代码的可用性有助于在现场进行进一步的研究和应用.
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