Accelerating Complex Genomic Design Tasks: AI-Guided gRNA Optimization for TP53 with GeneForgeLang

The CRISPR-Cas9 system has revolutionized genome engineering, but its clinical and research success hinges on the design of highly efficient and specific guide RNAs (gRNAs). This design process presents a complex multi-objective optimization challenge. Current computational approaches often rely on single-pass prediction models or require researchers to build bespoke, difficult-to-maintain scripting pipelines for iterative discovery workflows. Here, we introduce GeneForgeLang (GFL), a novel domain-specific language (DSL) designed to declaratively specify and orchestrate advanced AI-driven workflows in genomics. We demonstrate GFL's capabilities by applying its high-level guided_discovery abstraction to the problem of optimizing gRNAs for the human tumor suppressor gene TP53. Our workflow, defined in a single, readable GFL script, autonomously orchestrates an iterative cycle of candidate generation, predictive evaluation using deep learning models, and active learning-based selection. In just 5 cycles, the system efficiently evaluated 100 informative candidates, converging on solutions with near-optimal predicted performance scores (top score: 0.9859). GFL represents a new paradigm for enhancing the reproducibility, composability, and speed of computational research in the life sciences.