Convergence of Artificial Intelligence in Biotechnology: Innovations and Prospects

Abstract

Convergence of artificial intelligence with bionanotechnology shifts the «green» microbial synthesis of nanoparticles from an empirical approach to rational, data-driven design, enhancing reproducibility and technological maturity of the processes. Th e aim of this work was to summarize current knowledge and outline the role of AI, machine learning, and deep learning methods in multifactorial optimization of biosynthesis conditions, prediction of nanoparticle proper- ties prior to their production, guided self-assembly and engineering of producer strains, as well as in ensuring the safety of nanomaterials in line with the Safe-by-Design concept. Methods. Publications from 2020—2025 in PubMed, ACM, ScienceDirect, Google Scholar, and Scilit databases were analyzed, applying double screening and thematic synthesis. It was established that the use of AI significantly reduces the number of experiments, enables coordinated control of process parameters, ensures transfer of synthesis conditions between laboratory and pilot-scale setups, and allows ex-ante pre- diction of nanoparticle stability, bioactivity, and antimicrobial action. In particular, for La-doped ZnO nanoparticles, model accuracy reached R² ≈ 0.96. A promising direction is programmed self-assembly of nanoscale structures, algorithmic selection of surface functionalization, and control of the protein «corona», which determines biocompatibility and immune response. Another important result is the unification of toxicological data and improvement of regulatory compliance of products owing to explainable AI methods and integration with real-time process analytical control, as well as process design with quality built in from the outset. Th us, the convergence of artificial intelligence and «green» microbial synthesis establishes a platform for precision engineering of biogenic nanomaterials with predictable proper- ties, where strategic success depends on high-quality data, algorithm transparency, and interdisciplinary collaboration.