AI Guided Simulated Annealing for Automated Gene Editing Design

Abstract

Designing effective gene and mRNA sequences is a difficult optimisation problem because the number of possible nucleotide combinations grows extremely quickly with sequence length. Traditional optimisation methods such as simulated annealing are well suited to exploring these large search spaces, but their performance depends heavily on the quality of the scoring function used to evaluate candidate sequences. Hand crafted scoring rules are often slow to compute and cannot easily adapt to different biological contexts or patient specific constraints. This project presents an AI-guided simulated annealing framework for automated gene sequence design using two approaches. The first replaces fixed rule-based scoring with an adaptive model evaluating candidates using biological reference data and patient-specific information. By adjusting biological trait importance based on age, disease background, and treatment goals, the scoring model dynamically changes sequence evaluation without modifying the optimization algorithm. The second approach employs Gradient Boosting Regression on CRISPR guide RNA sequences with extracted biological features including GC content, positional nucleotides, and sequence complexity metrics. This model learns from validated literature guides, providing interpretable, deterministic scoring while maintaining adaptability. The framework is designed to support long running and repeated simulated annealing searches with minimal human intervention. Sequence evaluation is decoupled from the optimisation engine so that scoring models and reference databases can be updated as new experimental or clinical data becomes available. This allows the same optimisation pipeline to be reused across different applications such as vaccine design, cancer related gene targets or personalised therapies. By combining a fast native optimisation core with an adaptive and context aware evaluation model, this work demonstrates a flexible approach to large scale gene sequence optimisation. The proposed system highlights how AI driven scoring can improve the practicality of heuristic search methods and move sequence design closer to personalised and data driven biomedical applications.

Keywords: AI guided optimisation, Personalised Gene Design, Mrna Sequence Design, Adaptive Scoring Model, Computational Genomics, Patient Specific Modelling

DOI: 10.54941/ahfe1008117