SSHF-DTI:通过融合网络利用结构相似性和层次特征,用于药物向相互作用预测
Yuxiao Zhang1, Chengping Zhao1
1College of Electronics and Information Engineering, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, 610065, Sichuan, China.
Computational biology and chemistry
|December 27, 2025
概括
一个新的深度学习模型,SSHF-DTI,通过准确预测药物向相互作用 (DTI) 和结合亲和关系 (DTA) 来增强药物发现. 它整合了结构相似性和多源特征,以提高药物开发中的概括性和性能.
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.
- 药物发现 药物发现
背景情况:
- 预测药物向相互作用 (DTI) 和结合亲缘关系 (DTA) 是至关重要的,但由于实验成本而具有挑战性.
- 现有的深度学习模型往往缺乏跨领域的功能集成,限制了它们的预测能力和通用性.
研究的目的:
- 为预测DTI和DTA开发一个强大的和可泛化的深度学习模型.
- 提高药物发现中的计算方法的准确性和适用性.
主要方法:
- 拟议的SSHF-DTI模型整合结构上相似的信息和多源子结构特征.
- 通过使用坦尼莫托系数和摩根指纹进行数据丰富,将结构相似性纳入.
- 采用混合架构,将变压器和卷积元件结合起来,以实现层次特征融合.
主要成果:
- SSHF-DTI在预测准确度方面取得了显著的改进,在戴维斯数据集上,ROC-AUC和PR-AUC分别增加了0.031和0.147.
- 在药物相互作用 (DDI) 预测任务上表现出强大的概括能力.
- 在识别影响结合亲和力的分子结构特征方面表现出高灵敏度.
结论:
- SSHF-DTI为DTI,DTA和DDI预测提供了一个强大的和可通用的框架.
- 该模型有效地捕捉了复杂的层次特征相互作用,有望在药物发现和虚拟查方面取得进展.
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