AI-Powered Synthetic Biology: Current Situation, Challenges, and Future Perspectives
Abstract Synthetic biology has evolved from a set of engineering aspirations to an operationally sophisticated discipline, and artificial intelligence (AI) is its fastest-growing accelerant. This review traces that convergence across six interlocking domains: systems-level biological modeling, de novo protein engineering, metabolic and microbial programming, multi-omics data integration, regulatory element design, and clinical translation. For each domain, we survey established results, integrate findings from 2010–2026 literature, and articulate the trajectories that will define the next decade. Emerging themes include physics-informed neural networks for mechanistically constrained biological modeling, drug design, and federated learning architectures that allow global omics collaboration without centralizing sensitive data, self-driving laboratories that close the Design-Build-Test-Learn loop with minimal human intervention, and large language models that accelerate hypothesis generation from scientific literature. Alongside these opportunities, the review gives equal weight to the governance challenges they create: dual-use risks amplified by generative sequence design, the reproducibility crisis in AI-driven biodesign, and the equitable distribution of autonomous experimentation capacity. The overarching argument is that the promise of synthetic biology, making biological design as deliberate and reliable as any mature engineering discipline, is closer than ever, but will only be realized if technical ambition is matched by scientific rigor, transparent governance, and inclusive access.