Institute of Medical Intelligence, Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae414.
Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.
靶标识别是药物研发的关键任务之一，因为它有助于揭示草药/药物的作用机制并发现新的治疗靶标。尽管已经提出了多种草药靶标预测算法，但由于临床知识的不完整性和无监督模型的局限性，草药靶标的准确识别仍然面临数据和模型的巨大挑战。针对这一问题，我们提出了一种基于深度学习的靶标预测框架，称为 HTINet2，它设计了三个关键模块，即中药（TCM）和临床知识图谱嵌入、残差图表示学习和监督靶标预测。在第一个模块中，我们构建了一个大规模的知识图谱，涵盖了草药的 TCM 特性和临床治疗知识，并设计了一个深度知识嵌入组件，以学习草药和靶标的深度知识嵌入。在剩下的两个模块中，我们设计了一个类似于残差的图卷积网络来捕捉草药和靶标之间的深度交互，以及一个贝叶斯个性化排序损失来进行监督训练和靶标预测。最后，我们设计了全面的实验，与基线的比较表明 HTINet2 的性能优异（HR@10 提高了 122.7%，NDCG@10 提高了 35.7%），消融实验说明了 HTINet2 的设计模块的积极效果，案例研究证明了基于知识基础、文献和分子对接预测的青蒿和黄连靶标的可靠性。
