评估使用图形神经网络和转移学习用于口服生物可用性预测
1School of Chemistry, Chemistry Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
Journal of chemical information and modeling
|August 15, 2023
概括
带有转移学习的图形神经网络 (GNN) 准确预测口服生物可用性,这是药物发现的关键因素. 这种方法自动化了特征选择,改进了预测药物吸收的传统方法.
科学领域:
- 药理动力学 药理动力学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 口服生物可用性对于药物发现至关重要,影响治疗疗效.
- 传统的计算模型依赖于手动功能选择,需要领域专业知识和时间.
- 图形神经网络 (GNN) 提供自动化特征提取功能.
研究的目的:
- 用自动特征选择的GNN来预测口服生物可用性.
- 通过转移学习增强GNN性能,用于口服生物可用性预测.
主要方法:
- 利用图形神经网络 (GNN) 来从分子数据中提取特征.
- 员工通过预训练一种可溶性预测模型来转移学习.
- 应用了增强的GNN模型来预测口服生物可用性.
主要成果:
- 获得了0.797的口服生物可用性预测的平均准确度.
- 获得了F1得分为0.840和AUC-ROC为0.867.
- 使用相同测试数据集的先前研究的表现优于此前的研究.
结论:
- 具有转移学习的GNN提供了一种强大而有效的方法来预测口服生物可用性.
- 通过GNN自动选择功能,减少了对域专业知识的需求.
- 开发的模型显示了加速药物发现管道的巨大潜力.
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