使用图形注意力机制和分子指纹预测药物诱导的肝损伤
Jifeng Wang1, Li Zhang2, Jianqiang Sun3
1School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
Methods (San Diego, Calif.)
|December 1, 2023
概括
一个新的深度学习模型DMFPGA准确地预测了药物诱导的肝损伤 (DILI) 风险. 该工具通过识别潜在的肝毒性来帮助药物开发的早期阶段,改善安全评估.
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
- 人工智能的人工智能
背景情况:
- 药物诱导性肝损伤 (DILI) 在药物开发和临床使用中构成重大风险,可能导致严重的肝损伤或肝衰竭.
- 由于DILI的复杂而往往不太了解的病原性因素,因此需要先进的方法来进行早期风险评估.
研究的目的:
- 引入DMFPGA,这是一个新的深度学习模型,用于在药物开发的早期阶段预测DILI风险.
- 通过整合多样化的分子特征表示来提高DILI预测的准确性.
主要方法:
- 利用多头图的注意力机制,从分子图中提取化合物节点级别的特征.
- 结合多个分子指纹与基于图形的特征,以全面表示化合物特性.
- 使用完全连接的神经网络对化合物进行二进制分类,将其分类为DILI阳性或DILI阴性.
- 使用5倍交叉验证实验验证模型的性能.
主要成果:
- 与现有的四种最先进的计算方法相比,DMFPGA模型表现出更高的性能.
- 在预测DILI时获得了0.935的平均曲线下面积 (AUC) 和0.934的平均精度 (ACC).
- 分子指纹和图形特征的融合有效地捕获了化合物特性,以改善预测.
结论:
- DMFPGA提供了一个强大而准确的计算方法,用于早期预测和评估DILI风险.
- 该模型的高性能表明其在指导更安全的药物开发策略中的实用性.
- 这种深度学习框架推动了药物安全预测毒理学领域的发展.
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