Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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 re...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 25054 - 13
Main Authors: Li, Ying, Zhang, Xingyu, Chen, Zhuo, Yang, Hongye, Liu, Yuhui, Wang, Huiqing, Yan, Ting, Xiang, Jie, Wang, Bin
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 23.10.2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
631/154
Accuracy
Algorithms
Autoencoder framework
Chinese medicine
Computational Biology - methods
Datasets
Drug databases
Drug development
Drug discovery
Drug Discovery - methods
Drug Interactions
Drug Repositioning - methods
Drug-target interactions
Drugs, Chinese Herbal - chemistry
Drugs, Chinese Herbal - pharmacology
Herbal medicine
Heterogeneity
Humanities and Social Sciences
Humans
Medicine
Medicine, Chinese Traditional - methods
multidisciplinary
Noise reduction
Predictions
Science
Science (multidisciplinary)
Western medicine
Chinese medicine
Autoencoder framework
Western medicine
Drug databases
Drug-target interactions
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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 .
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76367-0