MSCMF-DTB:用于药物标结合预测的多规模交叉模式融合框架
Juan Huang1, Yuxue Pan1, Qu Chen2
1School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, People's Republic of China.
Scientific reports
|March 13, 2026
概括
这项研究介绍了MSCMF-DTB,这是一种用于预测药物向相互作用和亲和关系的深度学习模型. 该框架在各种数据集中表现出强的表现,有助于药物发现和重新利用.
科学领域:
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
- 在药理学中的机器学习.
背景情况:
- 预测药物标结合是至关重要的,但由于复杂的分子和序列数据而具有挑战性.
- 现有的模型难以整合多样化的数据类型,如分子拓,化学子结构和蛋白质序列.
- 准确的预测对于加速识别新疗法至关重要.
研究的目的:
- 开发一个端到端的深度学习框架,MSCMF-DTB,用于药物向相互作用 (DTI) 的分类和药物向 afinity (DTA) 的回归.
- 为了有效地整合分子图,化学子结构和蛋白质序列信息,以提高预测准确度.
- 为计算药物发现,虚拟查和药物重用提供一个多功能工具.
主要方法:
- MSCMF-DTB使用DenseGCN和指纹通道进行药物编码,并使用TAPE-BERT与1D CNN进行蛋白质序列分析.
- 一个交叉注意力机制和张量网络模型,用于更高阶特征相互作用的交叉模式药物-蛋白质关系.
- 合并的表示由多层感知器 (MLP) 处理,以进行最终的DTI和DTA预测.
主要成果:
- 在多个DTI (Human,C. elegans,GPCR,BioSNAP,DrugBank) 和DTA (DAVIS,KIBA) 数据集上,MSCMF-DTB取得了竞争性和一致的表现.
- 在DrugBank数据集中,MSCMF-DTB提高了AUC和RECALL的分别高达3.2%和6.1%,表现优于DrugBAN.
- 该模型在KIBA数据集上显示了稳定的DTA预测 (MSE:0.146,CI:0.886,r2:0.765) 并通过注意力分析确定了生物学相关的相互作用区域.
结论:
- MSCMF-DTB是一个有效的深度学习框架,用于预测药物向相互作用和亲和关系,整合各种分子和蛋白质数据.
- 该模型显示了对大型数据集的现有方法的显著改进,并提供了对药物标结合的可解释的见解.
- 它在冷启动案例研究中的成功应用凸显了它对虚拟查和药物重新定位努力的实际实用性.
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