A drug-drug interaction prediction method based on secure multi-party computing

Through the fusion of secure multi-party computing technology and multimodal features deep learning model, the problems of insufficient data and privacy protection in drug-drug interaction prediction are solved, and efficient drug interaction prediction and data collaboration are achieved.

CN115458043BActive Publication Date: 2025-08-29HUNAN UNIV
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Patent Information

Application Number
CN202211080663.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-05
Publication Date
2025-08-29
Estimated Expiration
2042-09-05

AI Technical Summary

Technical Problem

Existing deep learning models rely on insufficient high-quality data in drug-drug interaction predictions, and pharmaceutical companies' reluctance to share data leads to collaboration difficulties, affecting prediction performance.

Method used

Using secure multi-party computing technology, drug feature data is processed through secret sharing and encryption, multi-modal feature fusion deep learning model for prediction, and data from multiple institutions are integrated to collaborate without leaking private information.

Benefits of technology

It has achieved the improvement of the performance and data collaboration efficiency of drug-drug interaction prediction without revealing drug privacy information, and improved the accuracy and recall rate of prediction.

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Abstract

The present invention discloses a method for predicting drug-drug interactions based on secure multi-party computing, comprising the following steps: S1, obtaining a drug-drug interaction network, a drug-protein interaction network, a drug-disease association network, and a drug-side effect association network; S2, calculating Jaccard similarity based on different drug features to obtain similar features between all drugs, and using principal component analysis technology to reduce the dimensionality of all drug similarity features; S3, dividing each user's private feature data into four parts, encrypting them using secret sharing technology, and sending them to four servers using an additional secret sharing mechanism; S4, inputting the four parts of feature data into a preset private deep learning model to predict drug-drug interactions. The present invention enables high-quality collaboration between pharmaceutical companies and research institutions without leaking drug privacy information, thereby improving drug-drug interaction prediction.
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