域对抗性封闭的双线注意力网络跨域药物向相互作用预测
Wenhao Lv1, Qi Zhang2, Liwei Liu1
1College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Biochemical and biophysical research communications
|February 13, 2026
概括
这项研究介绍了GBAN-DA,这是一种深度学习模型,可以准确预测药物向相互作用 (DTI),并改善对新数据的概括. GBAN-DA通过提供对新目标的可靠预测来增强药物发现.
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.
- 机器学习是机器学习.
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于有效的药物发现至关重要.
- 现有的计算方法难以学习区分特征,并将其推广为新的分布移动数据.
研究的目的:
- 开发一个新的深度学习框架,GBAN-DA (带域调整的门式双线注意网络),用于准确和可概括的DTI预测.
- 解决当前DTI预测模型的局限性,特别是它们的概括能力.
主要方法:
- GBAN-DA集成了一种混合分子编码器 (GCN具有药物特征注意力,CNN-转换器用于蛋白质).
- 一个封闭的双线性注意力机制模拟了基层结构层面的相互作用.
- 条件域对抗网络 (CDAN) 调整跨域的特征分布,以提高概括性.
主要成果:
- 在域内任务中,GBAN-DA实现了最先进的性能,AUROC/AUPRC得分为0.964/0.950 (BindingDB) 和0.909/0.905 (BioSNAP).
- 域适应组件 (CDAN) 在冷对分割下显著提高了跨域预测11.9%的AUROC (BindingDB) 和12.9%的AUROC (BioSNAP).
- 案例研究验证了高可信度预测与实验数据库对象,如ABL1激酶和K. cathepsin K. 的实验数据库.
结论:
- GBAN-DA为准确和可概括的DTI预测提供了一个强大的框架,优于现有的模型.
- 拟议的领域适应策略有效地提高了现实世界药物查应用的模型概括性.
- GBAN-DA代表了计算药物发现的重大进步,促进了更有效地识别潜在的候选药物.
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