AI-Driven Enzyme Engineering Using All-Atom Protein Design and Molecular Dynamics-Based Functional Optimization

Abstract

Enzyme engineering increasingly depends on computational pipelines that combine generative protein design, all-atom structural modeling, molecular dynamics, and functional optimization. This paper examines the system-level architecture of AI-driven enzyme engineering as a socio-technical infrastructure rather than as a single model or isolated discovery technique. It argues that all-atom design and molecular dynamics are complementary but tension-laden subsystems: the former offers broad generative coverage and structural precision, while the latter provides dynamical and environmental realism at high computational cost. Their integration creates trade-offs among accuracy, throughput, interpretability, generalization, and experimental validation. The paper also considers deployment challenges in academic, industrial, and public-sector laboratories, including data provenance, compute equity, model maintenance, and reproducibility. Robustness must be evaluated not only through benchmark scores but through wet-lab transfer, mutation effects, solvent conditions, and long-horizon stability. Fairness concerns arise from unequal access to large-scale computing, proprietary structural databases, and automated design platforms, which can concentrate benefits in well-resourced institutions. Governance and policy implications include dual-use risks, biosafety review, intellectual property allocation, and environmental accountability for energy-intensive training and simulation. Through cross-domain comparisons with drug discovery and materials design, the paper identifies a need for layered validation, human oversight, and shared infrastructure. It concludes that AI-driven enzyme engineering will realize its promise only if technical optimization is embedded within accountable, sustainable, and inclusive systems.