A Blockchain-Enhanced Deep Learning Platform for Secure Semantic Alignment and Sharing of Chemical-Biological Data

Sida Huang, Jialuoyi Tan, Xie He, Jingxuan Liu, Zhiran Wang, Wenzhang Zhang, Yuji Dong

Abstract: The integration and sharing of chemical-biological data have become increasingly crucial in advancing research in drug discovery, materials science, and catalysis. However, several key challenges persist, including ensuring accurate semantic alignment, protecting data privacy, and providing reliable decision support. Addressing these challenges is essential to improving the efficiency and security of data-sharing and analysis processes. This paper proposes a comprehensive solution that leverages deep learning for semantic alignment and blockchain technology for secure data sharing. Our platform utilizes natural language processing (NLP) and graph neural networks (GNN) to align heterogeneous chemical-biological datasets, ensuring consistency and completeness across different sources. Additionally, blockchain technology is employed to establish a decentralized and tamper-resistant data-sharing framework, enhancing security and trust among stakeholders. Through extensive experimentation using the Open Catalyst dataset, our results demonstrate the effectiveness of the proposed approach in achieving high-precision data alignment, secure data transactions, and reliable decision support. This work presents an innovative and integrated platform that addresses long-standing challenges in chemical-biological data integration and sharing, paving the way for more efficient and secure collaborative research.

External IDs:dblp:conf/icccn/HuangTHLWZD25