🤖 AI Summary
This study addresses the critical limitation of conventional DNA synthesis screening, which exhibits a 100% false positive rate when encountering taxonomic families absent from reference databases, thereby failing to detect novel biothreats. To overcome this, the work introduces conformal risk control into the field for the first time, integrating multiple signals—k-mer Jaccard similarity, scores from a five-model large language model ensemble, and cosine similarity derived from embedding-based clustering—through a monotonic logical aggregator followed by rigorous calibration to strictly bound the false negative rate. Evaluated on a leave-one-family-out ten-fold cross-validation using UniProt KW-0800 toxin data, the method achieves zero false negatives across all folds and zero false positives in nine out of ten folds. The results indicate that performance is constrained not by algorithmic design but by the size of the calibration set, suggesting that expanding this set alone suffices to meet procurement-grade certification standards at α = 10⁻³.
📝 Abstract
DNA-synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists. We show that this baseline collapses to a 100% false-flag rate when the hazardous sequence comes from a taxonomic family absent from the reference set: under Conformal Risk Control's certified miss-rate constraint, a low-discrimination signal forces the threshold below the entire test-benign mass. We compose three signals derived from a synthesis order's public annotation: $k$-mer Jaccard similarity to known toxins, the trimmed-mean score of a five-LLM judge panel, and cosine similarity to clustered embedding centroids. Fused under a monotone logistic aggregator and calibrated by Conformal Risk Control, the resulting screener certifies $\mathbb{E}[\mathrm{FNR}] \le α$. Across ten leave-one-taxonomic-family-out folds at $α=0.05$ on UniProt KW-0800 reviewed toxins, the calibrated screener achieves 0% test miss rate on every fold and 0% test false-flag rate on nine of ten folds. The bound's finite-sample slack $1/(n_{\mathrm{cal}}+1)$ caps the certifiable miss rate at 1.77% on our 200-hazard subsample; reaching procurement-grade $α=10^{-3}$ requires an $18\times$ larger calibration set, which the full reviewed UniProt KW-0800 corpus is large enough to deliver. The binding constraint on certifiable DNA-synthesis screening is calibration data, not algorithms. Code: https://github.com/najmulhasan-code/crc-screen