对基于神经网络的方法进行全面的审查,以预测药物向相互作用
Fatemeh Panahandeh1, Najme Mansouri2
1Department of Computer Science, Shahid Bahonar University of Kerman, Box No. 76135-133, Kerman, Iran.
Molecular diversity
|July 27, 2025
概括
包括CNN和GNN在内的神经网络显著提升了药物发现的药物向相互作用预测. 集成多式联运数据的混合模型显示出卓越的性能,为新疗法铺平了道路.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 人工智能在药物发现中的作用
背景情况:
- 预测药物向相互作用 (DTI) 对于加速药物发现和重新定位至关重要.
- 传统的DTI预测方法在准确性和范围上都有局限性.
研究的目的:
- 审查和评估基于神经网络的DTI预测方法的有效性.
- 为突出应用深度学习到DTI分析的进步和挑战.
主要方法:
- 卷积神经网络 (CNN) 是一种神经网络.
- 图形神经网络 (GNN) 是一个神经网络.
- 具有自我注意机制的变压器架构.
- 整合多模式数据 (分子图,蛋白质序列)
主要成果:
- 神经网络的性能优于传统的DTI预测方法.
- 混合神经网络架构实现了高性能,AUROC为0.979.
- 多模式数据集成可以提高预测精度,减少错误.
结论:
- 神经网络,特别是混合模型,在计算药物发现中展示了变革的潜力.
- 未来的研究应该专注于解决诸如计算成本,可解释性和数据稀缺等挑战.
- 将多式联络数据与可解释的AI结合起来,可以打开新的治疗机会.
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