
Tempus
AI-enabled precision medicine for data-driven healthcare decisions.

Architecting precision genomic solutions through generative protein design and transformer-based sequence optimization.

GenomeEditing AI, pioneered by Profluent’s OpenCRISPR-1 architecture, represents a paradigm shift in biotechnology by moving from the discovery of natural enzymes to the synthesis of de novo gene editors. The platform utilizes Large Language Models (LLMs) trained on billions of protein sequences to generate functional CRISPR-Cas systems that do not exist in nature. By 2026, this technology has matured into a comprehensive enterprise suite that allows researchers to specify desired molecular properties—such as target specificity, reduced immunogenicity, and specific PAM requirements—and receive optimized genetic sequences for synthesis. The technical core relies on a transformer-based decoder that maps structure-to-function relationships, enabling the design of editors that are significantly smaller than Cas9, facilitating viral vector delivery. The market position of GenomeEditing AI is centered on democratizing access to high-precision editing tools while providing a robust API for pharmaceutical companies to integrate custom enzyme design into their internal drug discovery pipelines, effectively shortening the R&D cycle for gene therapies from years to months.
GenomeEditing AI, pioneered by Profluent’s OpenCRISPR-1 architecture, represents a paradigm shift in biotechnology by moving from the discovery of natural enzymes to the synthesis of de novo gene editors.
Explore all tools that specialize in analyze genomic data. This domain focus ensures GenomeEditing AI (by Profluent) delivers optimized results for this specific requirement.
Explore all tools that specialize in off-target risk prediction. This domain focus ensures GenomeEditing AI (by Profluent) delivers optimized results for this specific requirement.
Uses a transformer-based LLM to generate amino acid sequences that fold into functional CRISPR-like nucleases.
Algorithms designed to identify targeting possibilities outside traditional NGG PAM requirements.
Predicts T-cell epitope binding for generated proteins to minimize human immune response.
Combines deep learning with structural physics to predict genomic interactions with 99.8% accuracy.
Automatically simulates the stability of the protein-RNA-DNA ternary complex.
Codon optimization tailored specifically for AAV and LNP delivery systems.
Identifies functional gene editors without the need for evolutionary sequence homology.
Clone the official repository or access the web interface via OAuth2.
Define target genomic sequence and desired cell type context.
Select the generative model variant (e.g., OpenCRISPR-1 or Profluent-Enterprise).
Specify PAM constraints and maximum protein size for delivery vehicle compatibility.
Run the 'Design' task to generate 100+ de novo candidates.
Execute the 'Off-Target Analysis' module to filter candidates with <0.1% predicted mismatch.
Download structural PDB files for molecular dynamics validation.
Export optimized DNA sequences for commercial synthesis orders.
Submit candidates to the in-silico immunogenicity screening module.
Finalize design and move to wet-lab validation with provided experimental protocols.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its ability to generate truly novel enzymes and its high success rate in experimental validation, though some note the steep learning curve for non-bioinformaticians."
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