CF-DTI:用于增强药物向相互作用预测的粗细特征提取
Yining Qian1, Qingjie Wang2, Libang Yin2
1School of Computer Science and Technology, Northeastern University, Shenyang, 110819 China.
Health information science and systems
|September 8, 2025
概括
通过整合粗和细粒度特征,CF-DTI增强了药物向相互作用 (DTI) 的预测. 这种新的方法提高了准确性,并支持高效的药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物的发现和开发.
- 生物信息学是一种生物信息学.
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于有效的药物开发至关重要.
- 现有的DTI预测模型往往未充分利用多层次交互功能,从而限制了性能.
- 需要先进的模型来更有效地捕捉复杂的药物目标关系.
研究的目的:
- 开发一种新的粗细药物向药物相互作用模型 (CF-DTI).
- 通过整合粗粒度和细粒度特征来提高DTI预测的准确性.
- 改善药物发现中的数据利用和模型性能.
主要方法:
- 拟议的CF-DTI模型整合了粗粒和细粒的特征.
- 实现了一个信息过模块,具有用于特征提取的焦点和交叉注意力机制.
- 采用多细分学习和适应融合策略来实现特征集成.
主要成果:
- 在四个基准数据集中,CF-DTI的表现始终优于现有的基线模型.
- 在大规模数据集 (BindingDB,BioSNAP) 上获得了~2%的平均改进.
- 在AUROC和AUPRC中取得了显著的收益,特别是在未见对设置中 (6%的AUROC在BioSNAP上).
结论:
- CF-DTI有效地利用多层次的功能,实现卓越的DTI预测准确度.
- 该模型显示了降低预测模糊性和捕捉复杂分子相互作用的潜力.
- CF-DTI提供了一个有前途的工具来加速和增强药物发现过程.
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