Computational and artificial intelligence methodologies for big data analytics in structural biology

Abstract

High-throughput technologies have rapidly advanced, transforming structural biology and bioinformatics into data-intensive areas. Next-generation sequencing, cryo-electron microscopy (cryo-EM), high-resolution mass spectrometry, and large-scale interactomes generate unprecedented amounts of genomic, transcriptomic, proteomic, structural, and imaging data. This exponential growth of biological data poses significant computational and analytical hurdles, necessitating robust frameworks capable of integrating diverse data sources, minimizing noise, ensuring reproducibility, and providing biologically valuable insights. Protein structure prediction, drug discovery, functional annotation, and system-level studies have all been revolutionized by the emergence of machine learning, deep learning, and sophisticated statistical modeling as effective methods for handling this complexity. Despite these gains, many obstacles remain, including computing cost, data quality variance, algorithmic transparency, and concerns about data privacy and security concerns. This chapter reviews the major sources of biological big data, their defining characteristics, and the computational methodologies employed for their analysis, while surveying key applications across structural biology and bioinformatics. Special emphasis is placed on artificial intelligence (AI)-driven structure prediction, cryo-EM data processing, integrative modeling, multiomics analytics, and their growing impact on drug discovery and precision medicine. Emerging trends, such as explainable AI, quantum-enabled computation, and federated learning, are also discussed for their potential to enable interpretable, scalable, and privacy-preserving analytical frameworks. The synergy between big data analytics and experimental technologies will continue to speed up discoveries by enhancing mechanistic knowledge and facilitating translational advancements as it becomes an essential component of contemporary biological research.