Instructing a Chatbot to Design Nucleic Acid Probes for Diagnostics
Bibliographic record
Abstract
Large language models (LLMs) with their natural language processing and automation capabilities can streamline the design of nucleic acid diagnostic assays, but they can produce inaccurate outputs. Here, we developed an LLM-based automated design system for designing quantum dot barcode (QDB) and polymerase chain reaction (PCR) assays with high accuracy. We leveraged a structured prompting approach that combines domain knowledge, task planning, and tool use instructions. We used this system to design QDB assays for 1,512 genomes of infectious viruses in 24 h. We showed the versatility of this LLM system for generating PCR primers for infectious pathogens. We validated the QDB assay designs against 7 viruses with high epidemic-causing potential. Those QDB assays paired with the corresponding PCR products exhibit high analytical sensitivity (10 copies/μL). They also exhibited high specificity in multiplex respiratory and viral hemorrhagic fever panels. Our approach allows us to rapidly design nucleic acid diagnostics in epidemics or pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".