GeneTwin: A BioTech/DeepTech platform that uses high-fidelity machine learning models to simulate CRISPR base editor activity in silico. It predicts off-target effects and editing efficiency with 95% accuracy, allowing labs to 'pre-screen' thousands of guide RNAs digitally, reducing wet-lab iterations by 80% and cutting preclinical costs by $5M per candidate.

Gene therapy startups are facing 12-month delays in clinical trials because they cannot validate the specificity of their CRISPR base editors offline. Current in-vitro validation is slow and prone to false negatives, leading to wasted $10M+ in preclinical work. Labs lack a standardized, high-throughput digital twin for testing editor efficiency before wet-lab execution.