Omnii Defends Against AI-Designed Pathogens with Function-Aware DNA Screening

Leaders of OpenAI, Anthropic, & GDM signed a letter urging Congress to mandate DNA synthesis screening. But chatbots & agents can't read DNA. So we built Omnii to defend against natural & AI-designed pathogens. 🖥️ Blog: https://lnkd.in/gx9stCYb 📃 Letter: screendna.org Today anyone can design DNA & get it shipped like an Amazon order from synthesis companies. This powers real science, but it's a biosecurity risk if bad actors design threats. AI raises the stakes: our prior model, Evo, generated the first AI-designed phage genome Our frontier genome language model, Omnii, reads and writes DNA/RNA and also understands proteins. It's function-aware: it screens whole genomes, not just genes/proteins, & flags threats with dangerous function even when unlike known pathogens. Ergo: generalized detection. Today's screening asks: "have we seen this before?" — aligning a sequence against known-threat databases. But sequence ≠ function. Distant proteins can fold alike, like dengue & chikungunya glycoproteins. Alignment misses this, and so we must screen beyond proteins. The hard cases are "paraphrases": same dangerous function, different sequence. Evolution designs them; a generative model designs them on demand. The kicker: a 442-residue protein can have ~10¹⁵⁶ viable variants. Enumeration is intractable. It's the blind spot of current tools. Omnii closes the gap: pathogenic functional signals live in its embedding geometry. Paraphrased pathogens cluster with their pathogenic templates, not harmless lookalikes, even when no alignment tool flags the link. A linear probe separates the classes at AUROC 0.991. Across 9 detection systems (industrial screeners, protein & genome LMs), Omnii leads on paraphrase F1 score. Its zero-shot score already matches or beats every baseline's multi-shot score. Less supervision, more signal. And it's not just reading structure. We stress-tested with paraphrases built to keep function but break the predicted fold. Structure-only detectors lose their main signal. Omnii degrades the least, anchoring on sequence + annotation, and not protein folds alone. Beyond single proteins, a virus is a whole system: regulatory & structural RNA set replication, host range, transmissibility. We wanted to see if Omnii could predict viral fitness effects, a proxy for host risk, on a dengue (DENV-2) deep mutational scan. Omnii predicts fitness effects from sequence alone, sans alignment. Omnii even shows structure-awareness: its strongest signal is on medium-range interactions (100-1024 nt) that fold a flavivirus on itself. Omnii reads a variant's structural environment, not just its neighbors. If models become function-aware, our defenses must too. To scale biodefense, we're scaling our team + partnerships. 🌉🌎🗼Join our mission in SF/Tokyo: https://lnkd.in/gMa7anFQ  📝 Sign up for early access: https://lnkd.in/gewyGyHk

Leaders of OpenAI, Anthropic, & GDM signed a letter urging Congress to mandate DNA synthesis screening. But chatbots & agents can't read DNA. So we built Omnii to defend against natural & AI-designed pathogens. 🖥️ Blog: https://lnkd.in/gx9stCYb 📃 Letter: screendna.org Today anyone can design DNA & get it shipped like an Amazon order from synthesis companies. This powers real science, but it's a biosecurity risk if bad actors design threats. AI raises the stakes: our prior model, Evo, generated the first AI-designed phage genome Our frontier genome language model, Omnii, reads and writes DNA/RNA and also understands proteins. It's function-aware: it screens whole genomes, not just genes/proteins, & flags threats with dangerous function even when unlike known pathogens. Ergo: generalized detection. Today's screening asks: "have we seen this before?" — aligning a sequence against known-threat databases. But sequence ≠ function. Distant proteins can fold alike, like dengue & chikungunya glycoproteins. Alignment misses this, and so we must screen beyond proteins. The hard cases are "paraphrases": same dangerous function, different sequence. Evolution designs them; a generative model designs them on demand. The kicker: a 442-residue protein can have ~10¹⁵⁶ viable variants. Enumeration is intractable. It's the blind spot of current tools. Omnii closes the gap: pathogenic functional signals live in its embedding geometry. Paraphrased pathogens cluster with their pathogenic templates, not harmless lookalikes, even when no alignment tool flags the link. A linear probe separates the classes at AUROC 0.991. Across 9 detection systems (industrial screeners, protein & genome LMs), Omnii leads on paraphrase F1 score. Its zero-shot score already matches or beats every baseline's multi-shot score. Less supervision, more signal. And it's not just reading structure. We stress-tested with paraphrases built to keep function but break the predicted fold. Structure-only detectors lose their main signal. Omnii degrades the least, anchoring on sequence + annotation, and not protein folds alone. Beyond single proteins, a virus is a whole system: regulatory & structural RNA set replication, host range, transmissibility. We wanted to see if Omnii could predict viral fitness effects, a proxy for host risk, on a dengue (DENV-2) deep mutational scan. Omnii predicts fitness effects from sequence alone, sans alignment. Omnii even shows structure-awareness: its strongest signal is on medium-range interactions (100-1024 nt) that fold a flavivirus on itself. Omnii reads a variant's structural environment, not just its neighbors. If models become function-aware, our defenses must too. To scale biodefense, we're scaling our team + partnerships. 🌉🌎🗼Join our mission in SF/Tokyo: https://lnkd.in/gMa7anFQ  📝 Sign up for early access: https://lnkd.in/gewyGyHk

Leaders of OpenAI, Anthropic, & GDM signed a letter urging Congress to mandate DNA synthesis screening. But chatbots & agents can't read DNA. So we built Omnii to defend against natural & AI-designed pathogens. 🖥️ Blog: https://lnkd.in/gx9stCYb 📃 Letter: screendna.org Today anyone can design DNA & get it shipped like an Amazon order from synthesis companies. This powers real science, but it's a biosecurity risk if bad actors design threats. AI raises the stakes: our prior model, Evo, generated the first AI-designed phage genome Our frontier genome language model, Omnii, reads and writes DNA/RNA and also understands proteins. It's function-aware: it screens whole genomes, not just genes/proteins, & flags threats with dangerous function even when unlike known pathogens. Ergo: generalized detection. Today's screening asks: "have we seen this before?" — aligning a sequence against known-threat databases. But sequence ≠ function. Distant proteins can fold alike, like dengue & chikungunya glycoproteins. Alignment misses this, and so we must screen beyond proteins. The hard cases are "paraphrases": same dangerous function, different sequence. Evolution designs them; a generative model designs them on demand. The kicker: a 442-residue protein can have ~10¹⁵⁶ viable variants. Enumeration is intractable. It's the blind spot of current tools. Omnii closes the gap: pathogenic functional signals live in its embedding geometry. Paraphrased pathogens cluster with their pathogenic templates, not harmless lookalikes, even when no alignment tool flags the link. A linear probe separates the classes at AUROC 0.991. Across 9 detection systems (industrial screeners, protein & genome LMs), Omnii leads on paraphrase F1 score. Its zero-shot score already matches or beats every baseline's multi-shot score. Less supervision, more signal. And it's not just reading structure. We stress-tested with paraphrases built to keep function but break the predicted fold. Structure-only detectors lose their main signal. Omnii degrades the least, anchoring on sequence + annotation, and not protein folds alone. Beyond single proteins, a virus is a whole system: regulatory & structural RNA set replication, host range, transmissibility. We wanted to see if Omnii could predict viral fitness effects, a proxy for host risk, on a dengue (DENV-2) deep mutational scan. Omnii predicts fitness effects from sequence alone, sans alignment. Omnii even shows structure-awareness: its strongest signal is on medium-range interactions (100-1024 nt) that fold a flavivirus on itself. Omnii reads a variant's structural environment, not just its neighbors. If models become function-aware, our defenses must too. To scale biodefense, we're scaling our team + partnerships. 🌉🌎🗼Join our mission in SF/Tokyo: https://lnkd.in/gMa7anFQ  📝 Sign up for early access: https://lnkd.in/gewyGyHk

Leaders of OpenAI, Anthropic, & GDM signed a letter urging Congress to mandate DNA synthesis screening. But chatbots & agents can't read DNA. So we built Omnii to defend against natural & AI-designed pathogens. 🖥️ Blog: https://lnkd.in/gx9stCYb 📃 Letter: screendna.org Today anyone can design DNA & get it shipped like an Amazon order from synthesis companies. This powers real science, but it's a biosecurity risk if bad actors design threats. AI raises the stakes: our prior model, Evo, generated the first AI-designed phage genome Our frontier genome language model, Omnii, reads and writes DNA/RNA and also understands proteins. It's function-aware: it screens whole genomes, not just genes/proteins, & flags threats with dangerous function even when unlike known pathogens. Ergo: generalized detection. Today's screening asks: "have we seen this before?" — aligning a sequence against known-threat databases. But sequence ≠ function. Distant proteins can fold alike, like dengue & chikungunya glycoproteins. Alignment misses this, and so we must screen beyond proteins. The hard cases are "paraphrases": same dangerous function, different sequence. Evolution designs them; a generative model designs them on demand. The kicker: a 442-residue protein can have ~10¹⁵⁶ viable variants. Enumeration is intractable. It's the blind spot of current tools. Omnii closes the gap: pathogenic functional signals live in its embedding geometry. Paraphrased pathogens cluster with their pathogenic templates, not harmless lookalikes, even when no alignment tool flags the link. A linear probe separates the classes at AUROC 0.991. Across 9 detection systems (industrial screeners, protein & genome LMs), Omnii leads on paraphrase F1 score. Its zero-shot score already matches or beats every baseline's multi-shot score. Less supervision, more signal. And it's not just reading structure. We stress-tested with paraphrases built to keep function but break the predicted fold. Structure-only detectors lose their main signal. Omnii degrades the least, anchoring on sequence + annotation, and not protein folds alone. Beyond single proteins, a virus is a whole system: regulatory & structural RNA set replication, host range, transmissibility. We wanted to see if Omnii could predict viral fitness effects, a proxy for host risk, on a dengue (DENV-2) deep mutational scan. Omnii predicts fitness effects from sequence alone, sans alignment. Omnii even shows structure-awareness: its strongest signal is on medium-range interactions (100-1024 nt) that fold a flavivirus on itself. Omnii reads a variant's structural environment, not just its neighbors. If models become function-aware, our defenses must too. To scale biodefense, we're scaling our team + partnerships. 🌉🌎🗼Join our mission in SF/Tokyo: https://lnkd.in/gMa7anFQ  📝 Sign up for early access: https://lnkd.in/gewyGyHk