Quantum Algorithms, Drug Discovery

Quanome and QAI Labs combine AI and quantum algorithms

Avatar of Ivy Delaney
November 9, 2026 by Ivy Delaney

Quanome Technologies will issue 6,541,236 new shares of its common stock, representing approximately 15.97% of outstanding shares after issuance, in exchange for a roughly 5% stake in QAI Labs, a company developing the NOVA-3 platform. NOVA-3 combines quantum-computing algorithms, run on classical simulators, to accelerate multi-specific antibody discovery. According to Quanome CEO Yang Li, “This investment agreement puts another of Quanome’s quantum and AI strategy streams into execution.” The deal aims to improve the speed and precision of molecular discovery by prioritizing candidates for laboratory evaluation.

Quanome’s 5% Equity Investment in QAI Labs Advances Life Sciences

QAI Labs will issue 31,579 new shares to Quanome, representing approximately 5% equity interest in QAI Labs immediately after the issuance. This targeted investment reflects Quanome’s strategy of securing positions in emerging technologies with high-value applications, rather than pursuing broad-based acquisitions. The transaction value assigned to the shares is US$5.0 million, establishing an implied equity valuation of approximately US$100 million for QAI Labs. The core of QAI Labs’ approach lies in its NOVA-3 platform, which uniquely integrates AI-driven design validation and exploration of quantum-computing algorithms running on classical simulators.

This hybrid approach is not a full deployment of quantum computers, but a pragmatic step toward using quantum principles within the constraints of current technology. NOVA-3 begins with a curated library of antibody building blocks, using artificial intelligence and molecular modeling to assemble and rank potential combinations before any laboratory work begins.

This prioritization is intended to streamline research and focus resources on the most promising candidates. “Our objective is to use AI to identify and rank promising designs, test where quantum-enhanced approaches can add value to difficult calculations, and validate the selected outputs,” said Dr. Daniel Feng, Co-Founder of QAI Labs. The integration of results back into the design cycle is central to NOVA-3’s function, creating a feedback loop intended to refine candidate selection and improve the efficiency of the discovery process.

QAI Labs currently holds two in-licensed programs, IMT030122 and IMB030202, both at an early preclinical stage, though no studies to enable Investigational New Drug applications have begun. While promising, these programs and the NOVA-3 platform remain pre-commercial, requiring extensive laboratory validation, clinical development and regulatory approval before any potential medicines can reach patients.

Quanome’s investment is intended to accelerate the evaluation and development of NOVA-3, expanding its scientific reach and potential for partnerships. The company will use its Scientific Advisory Network and broader ecosystem to strengthen scientific review, technical capabilities and validation pathways.

“QAI Labs is building a more focused engine for early-stage molecular discovery: using advanced computing to identify promising multi-specific antibody candidates sooner, then validating the strongest designs. The platform aims to improve the pace and precision of molecule discovery and help create better treatment options for patients. We believe this may represent a compelling long-term opportunity for us.” The potential of AI and quantum-enhanced methods lies in their ability to compress design cycles and sharpen candidate selection, directing laboratory resources toward molecules with the highest probability of success.

These technologies do not eliminate the need for rigorous clinical trials and regulatory review. But they can create a more efficient and targeted path from digital design to laboratory validation, potentially accelerating the development of new medical treatments. Quanome intends to pursue a strategic collaboration with QAI Labs in AI-driven drug discovery following the closing of the investment, though QAI Labs will continue to operate independently.

This investment agreement puts another of Quanome’s quantum and AI strategy streams into execution.

Yang Li, Chief Executive Officer of Quanome Technologies
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Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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