Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Department of Pathology, Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, China.
Sci Rep. 2024 Oct 23;14(1):25054. doi: 10.1038/s41598-024-76367-0.
Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and preprocessing of data on ingredients and targets, we employ an innovative autoencoder framework to fuse multiple drug (target) topological similarity matrices. Additionally, we employ denoising blocks, sparse blocks, and stacked blocks to extract crucial features from the similarity matrix, reducing noise and enhancing accuracy across diverse datasets. Our results indicate that the CWI-DTI model shows improved performance compared to several existing state-of-the-art methods on the datasets tested in both Western and Chinese medicine databases. The findings of this study hold immense promise for advancing DTI prediction in Chinese and Western medicine, thus fostering more efficient drug discovery and repurposing endeavors. Our model is available at https://github.com/WANG-BIN-LAB/CWIDTI .
准确预测药物-靶点相互作用（DTIs）对于推进药物发现和再利用至关重要。计算方法显著提高了西药中药物-靶点相互作用实验预测的效率。然而，由于中药成分和靶点的数量庞大且高度异质，准确预测它们之间的复杂关系仍然是一个巨大的挑战。在这项研究中，我们引入了 CWI-DTI 方法，该方法使用药物成分或候选靶点的交互关系的大型数据集实现了 DTI 的高精度预测。此外，我们还提出了一个新的数据集来评估中、西药的预测准确性。通过对成分和靶点数据的精心收集和预处理，我们采用创新的自动编码器框架融合多个药物（目标）拓扑相似性矩阵。此外，我们还采用去噪块、稀疏块和堆叠块从相似性矩阵中提取关键特征，减少了不同数据集的噪声并提高了准确性。我们的结果表明，与在中、西药数据库中测试的几个现有最先进的方法相比，CWI-DTI 模型在数据集上的表现有所提高。这项研究的结果为推进中、西药的 DTI 预测提供了巨大的希望，从而促进了更有效的药物发现和再利用努力。我们的模型可在 https://github.com/WANG-BIN-LAB/CWIDTI 上获得。
