From prompts to genomes: A constraint-first framework for AI-driven sequence design across life and viruses
- 1Aether Biomics, Tanah Abang, Jakarta, Indonesia
- 2Department of Biotechnology, School of Health and Life Sciences, i3L University, Pulo Gadung, Jakarta, Indonesia
Abstract
A constraint-first framework for future ‘prompt-to-genome’ AI across Bacteria, Archaea, Eukaryota, and viruses is proposed. Genome-scale models can now generate long DNA sequences, and experimentally viable genome-scale designs have begun to appear, but sequence generation alone does not establish biological feasibility. Constraints should not require designs to remain within natural sequence space; rather, they should define testable boundaries for function, inheritance, lifecycle coherence and safe use, including new-to-nature designs. We propose a two-part system. First, a Constraint Compiler translates natural-language prompts into machine-checkable specifications defining scope, context, objectives, assumptions and governance requirements. Second, a modular Validator Stack (C1–C6) evaluates candidate designs for sequence integrity, gene and regulatory plausibility, genome completeness, comparative coherence, systems-level feasibility, and safety and governance. Extreme prompts serve as stress tests to expose hidden assumptions, conflicting objectives, and refusal points, complemented by adversarial testing and risk assessment. A phased roadmap and community benchmarks are proposed to measure specification accuracy, biological feasibility, robustness, and governance performance. The goal is to move prompt-to-genome systems from impressive generation toward reliable, auditable, and accountable biological design.
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BibTeX
@article{wicaksono2026from,
title = {{From prompts to genomes: A constraint-first framework for AI-driven sequence design across life and viruses}},
author = {Adhityo Wicaksono and Arli Aditya Parikesit},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1014865},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014865},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From prompts to genomes: A constraint-first framework for AI-driven sequence design across life and viruses
AU - Adhityo Wicaksono
AU - Arli Aditya Parikesit
PY - 2026
DA - 2026/10/15
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014865
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014865
AB - A constraint-first framework for future ‘prompt-to-genome’ AI across Bacteria, Archaea, Eukaryota, and viruses is proposed. Genome-scale models can now generate long DNA sequences, and experimentally viable genome-scale designs have begun to appear, but sequence generation alone does not establish biological feasibility. Constraints should not require designs to remain within natural sequence space; rather, they should define testable boundaries for function, inheritance, lifecycle coherence and safe use, including new-to-nature designs. We propose a two-part system. First, a Constraint Compiler translates natural-language prompts into machine-checkable specifications defining scope, context, objectives, assumptions and governance requirements. Second, a modular Validator Stack (C1–C6) evaluates candidate designs for sequence integrity, gene and regulatory plausibility, genome completeness, comparative coherence, systems-level feasibility, and safety and governance. Extreme prompts serve as stress tests to expose hidden assumptions, conflicting objectives, and refusal points, complemented by adversarial testing and risk assessment. A phased roadmap and community benchmarks are proposed to measure specification accuracy, biological feasibility, robustness, and governance performance. The goal is to move prompt-to-genome systems from impressive generation toward reliable, auditable, and accountable biological design.
SN - 1553-7358
ER -