Decoy Therapeutics Teams with Quantori, Boosting AI-Driven Peptide Discovery Speed by Up to 100x
Published: 17 Mar 2026
Author: Precedence Research
On March 2026, Decoy Therapeutics (NASDAQ: DCOY) announced a strategic partnership with Quantori, backed by funding from Google Cloud, aimed at revolutionizing antiviral drug discovery by dramatically increasing computational speed. Through this collaboration, Decoy plans to boost its peptide design and molecular simulation capabilities by 25 to 100 times, potentially transforming how quickly new antiviral drug candidates are discovered, optimized, and validated.
At the heart of the partnership is the creation of a Google Cloud-native computational platform that brings together cutting-edge AI-driven protein folding models, molecular dynamics simulations, and performance-optimization tools in a single environment. This integration is expected to enhance the flexibility and speed of Decoy’s proprietary IMPACT™ engine, streamlining the process from initial sequence generation all the way to structural validation. The ultimate goal is to shorten the path from digital discovery to potential clinical candidates, making drug development faster and more efficient.
A major focus of the collaboration is the design of multi-antiviral drugs, known as D-MAVs™, capable of targeting multiple viruses at once. This approach could enable a single therapeutic to prevent and treat various respiratory viruses, addressing both commercial and public health needs through a more efficient, multi-target pipeline.
The partnership also emphasizes building a robust, future-ready infrastructure. With tiered protein folding pipelines, GPU-accelerated molecular dynamics, and diffusion-based modeling, Decoy can refine current peptides while quickly incorporating next-generation AI models as they become available. The platform also offers real-time analytics and visualization, improving the feedback loop between computational predictions and laboratory experiments. Automated precision optimization ensures an effective balance between computational speed and model accuracy, freeing researchers to focus on scientific discovery rather than technical bottlenecks.
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