Quantifying evolutionary novelty and design efficiency in generative genome design

Abstract

Generative genome design models can now produce previously unobserved genome-length sequences, but assessing their capabilities is complicated by limitations in functional prediction. The ability to engineer genomes faster than we can understand them risks creating biosecurity vulnerabilities. To evaluate these potential risks systematically, we propose a framework that distinguishes between (i) evolutionary novelty, quantified through phylogenetic and sequence similarity to natural genomes; and (ii) design efficiency - the efficiency with which a model finds viable sequences compared to simple baseline generators. Applying this framework to bacteriophages designed by the genome language model Evo 2, we find that model likelihood strongly predicts experimental viability, capturing functional constraints beyond simple biological heuristics. However, this efficiency derives largely from staying close to previously observed sequences rather than exploring novel sequence space, reflecting the combined performance of the model and additional filters that were applied to its outputs. Compared to baselines of random mutagenesis and serial passage, the model achieves substantial design efficiency while its outputs remain phylogenetically close to natural genomes. We conclude that the generative capabilities of Evo 2 warrant low to moderate biosecurity concern for de novo hazard creation, although the degree to which these findings generalise to larger or less constrained viral architectures is an open question. Our framework enables an evidence-based capability assessment of generative genome design tools, informing future biosecurity evaluations.