This review discusses recent computational approaches aimed at enhancing the precision and efficiency of the CRISPR-Cas9 gene editing system. These approaches leverage data-driven biophysical modeling, integrating insights from high-throughput experimental datasets and atomistic molecular dynamics simulations to elucidate the underlying molecular mechanisms. We evaluate these computational frameworks in terms of their ability to accurately predict CRISPR-Cas9 cleavage efficiency. Importantly, we highlight how the synergy between computational modeling and experimental validation accelerates the development of more robust predictive tools, while also minimizing the reliance on costly trial-and-error strategies in single-guide RNA (sgRNA) design. Looking ahead, the implementation of closed-loop feedback systems, where computational predictions guide experiments and experimental outcomes refine models, will be essential for realizing the full potential of CRISPR-based therapeutics and applications.
Liyanage et al. (Thu,) studied this question.