Molecular biology in the exabyte era: Taming the data deluge for biological revelation and clinical transformation.

The explosive growth in next-generation high-throughput technologies has driven modern molecular biology into the exabyte era, producing an unparalleled volume of biological data across genomics, proteomics, metabolomics, and biomedical imaging. Although this massive expansion of data can power future biological discoveries and precision medicine, it presents considerable challenges, including computational bottlenecks, fragmented data landscapes, and ethical issues related to privacy and accessibility. We highlight novel contributions, such as the application of blockchain technologies to ensure data integrity and traceability, a relatively underexplored solution in this context. We describe how artificial intelligence (AI), machine learning (ML), and cloud computing fundamentally reshape and provide scalable solutions for these challenges by enabling near real-time pattern recognition, predictive modelling, and integrated data analysis. In particular, the use of federated learning models allows privacy-preserving collaboration across institutions. We emphasise the importance of open science, FAIR principles (Findable, Accessible, Interoperable, and Reusable), and blockchain-based audit trails to enhance global collaboration, reproducibility, and data security. By processing multi-omics datasets in integrated formats, we can enhance our understanding of disease mechanisms, facilitate biomarker discovery, and develop AI-assisted, personalised therapeutics. Addressing these technical and ethical demands requires robust governance frameworks that protect sensitive data without hindering innovation. This paper underscores a shift toward more secure, transparent, and collaborative biomedical research, marking a decisive step toward clinical transformation.