ABSTRACT:
A Computational biology and informatics have provided a much-needed platform for nucleoside applications in the B2B drug discovery and gene editing segment. The key virtues of Industry 5.0- bioinformatics, machine learning, and big data upon amalgamation with computational biology and informatics have exposed the enormous potential of nucleoside in B2B domains of drug discovery and gene editing. Nucleosides are compounds used in pharmaceutical, biotechnology, and chemical domains and not by individual consumers; hence, they are B2B products. This bibliometric analysis aims to study the research trends in the role of computational biology and informatics in designing nucleoside analogs, predicting pharmacokinetics and pharmacodynamics, addressing the computational-experimental integration challenges, and identifying emerging trends of industry 5.0 in machine learning applications from 2021 to 2025 using Web of Science database (WOS). This study shows research trends and patterns through citations and co-occurrence analysis and also identifies the key authors and leading journals from 413 articles. The results show steady growth in research till 2023 but a drop in research since 2024, indicating the challenges posed due to the integration gap between silico models and laboratory results. The insights from the analysis lay a strong foundation for future research to achieve predictive accuracy and optimize drug development. Future studies must utilize machine learning and big data analytics for nucleoside analog design, predictive modelling for ADME profiling, and enhanced experimental validation framework to advance nucleoside-based therapeutics and gene editing.