Fan Shuyue, Yang Kuo, Lu Kezhi, Dong Xin, Li Xianan, Zhu Qiang, Li Shao, Zeng Jianyang, Zhou Xuezhong
Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
Faculty of Engineering and IT, Australian AI Institute, University of Technology Sydney, Sydney, NSW 2007, Australia.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae692.
Drug repositioning (DR), identifying novel indications for approved drugs, is a cost-effective strategy in drug discovery. Despite numerous proposed DR models, integrating network-based features, differential gene expression, and chemical structures for high-performance DR remains challenging.
We propose a comprehensive deep pretraining and fine-tuning framework for DR, termed DrugRepPT. Initially, we design a graph pretraining module employing model-augmented contrastive learning on a vast drug-disease heterogeneous graph to capture nuanced interactions and expression perturbations after intervention. Subsequently, we introduce a fine-tuning module leveraging a graph residual-like convolution network to elucidate intricate interactions between diseases and drugs. Moreover, a Bayesian multiloss approach is introduced to balance the existence and effectiveness of drug treatment effectively. Extensive experiments showcase the efficacy of our framework, with DrugRepPT exhibiting remarkable performance improvements compared to SOTA (state of the arts) baseline methods (improvement 106.13% on Hit@1 and 54.45% on mean reciprocal rank). The reliability of predicted results is further validated through two case studies, i.e. gastritis and fatty liver, via literature validation, network medicine analysis, and docking screening.
The code and results are available at https://github.com/2020MEAI/DrugRepPT.
药物重新定位（DR），即确定已批准药物的新适应症，是药物研发中一种具有成本效益的策略。尽管提出了众多的DR模型，但将基于网络的特征、差异基因表达和化学结构整合起来以实现高性能的DR仍然具有挑战性。
我们提出了一种用于DR的全面深度预训练和微调框架，称为DrugRepPT。首先，我们设计了一个图预训练模块，在一个庞大的药物-疾病异构图上采用模型增强对比学习，以捕捉干预后的细微相互作用和表达扰动。随后，我们引入了一个微调模块，利用类似图残差的卷积网络来阐明疾病和药物之间的复杂相互作用。此外，还引入了一种贝叶斯多损失方法来有效平衡药物治疗的存在性和有效性。广泛的实验展示了我们框架的有效性，与最先进（SOTA）基线方法相比，DrugRepPT表现出显著的性能提升（在Hit@1上提高了106.13%，在平均倒数排名上提高了54.45%）。通过胃炎和脂肪肝这两个案例研究，通过文献验证、网络医学分析和对接筛选，进一步验证了预测结果的可靠性。
