Details

Autor(en) / Beteiligte

• Xiao, Xia
• Wang, Xiaoqi
• Liu, Shengyun
• Peng, Shaoliang

Titel

MPCDDI: A Secure Multiparty Computation-Based Deep Learning Framework for Drug-Drug Interaction Predictions

Ist Teil von

• Bioinformatics Research and Applications, 2023, Vol.13760, p.263-274

Ort / Verlag

Switzerland: Springer

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multi-source data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the inter-institutional collaborations for DDI predictions. We propose MPCDDI, a secure multiparty computation-based deep learning framework for drug-drug interaction predictions. MPCDDI leverages the secret sharing technologies to incorporate the drug-related feature data from multiple institutions and develops a deep learning model for DDI predictions. In MPCDDI, all data transmission and deep learning operations are integrated into secure multiparty computation (MPC) frameworks to enable high-quality collaboration among pharmaceutical institutions without divulging private drug-related information. The results suggest that MPCDDI is superior to other five baselines and achieves the similar performance to that of the corresponding plaintext collaborations. More interestingly, MPCDDI significantly outperforms methods that use private data from the single institution. In summary, MPCDDI is an effective framework for promoting collaborative and privacy-preserving drug discovery.