Unlocking Secure, Targeted Drug Design with Federated AI and Node-Level Control
- David Brooks
- Aug 7
- 3 min read
At Qubigen, we believe that world-class drug design demands more than just high-quality datasets or cutting-edge algorithms, it also requires knowing how and where to apply them. Cutting-edge molecular design and quality candidate generation shouldn’t come at the expense of data privacy. That’s why we’ve built a next-generation Federated AI framework that enables secure, distributed, and intelligent drug design, without ever exposing sensitive or proprietary data.
Federated AI for Secure, Fine-Tuned Drug Design
While traditional distributed federated learning addresses data security, it often suffers from high network overhead, poor scalability, and slow costly training, even for a single iteration, making it impractical for real-world drug design applications [1].
Qubigen’s infrastructure is purpose-built for this challenge. Our decentralized, microservice-driven architecture manages AI model training across a network of nodes, each operating independently under strict data privacy controls. Whether deployed within pharma companies, or between academic centers, each node can train models locally to collectively develop a larger, more robust Federated AI model. This capability is not theoretical, it's already patented and tested in real-world environments.
Dual-Powered Design: Privacy and Precision
Qubigen takes a new, fast and cost-efficient approach, our Federated AI does two things simultaneously [2]:
Privacy-Preserving Data Utilization
Our patented method [US20220344049A1] enables the virtual training of AI on sensitive datasets without needing to move or view the data. Qubigen preserves IP and privacy across decentralized datasets, ideal for multi-institutional collaborations, dispersed pharma locations, subsidiaries, or branches, or CRO networks. Qubigen can, for example, allow an organization to securely leverage insights from a federated network of data from around the globe, including it's own contribution and Qubigen’s own extensive database, to facilitate breakthroughs that no single company could achieve on their own.
Crafted AI for Drug-Like Properties
Our platform enables fine-tuned control of drug-like characteristics, such as physicochemical features, various ADMET properties, and target specificity at each node. By combining in-house algorithm design with secure, microservice-driven orchestration, we generate AI models tailored to specific therapeutic goals, all without compromising data security.
This dual-function approach means partners can generate custom, properly-optimized compounds without ever revealing internal data or experimental history. The method has been well-tested for a range of biomedical datatypes [1,3].
The Expertise Behind Qubigen’s Federated AI
“Qubigen’s value lies not just in our curated datasets, FedAI platform, or modelling systems, but in our world-class AI engineering and data science expertise, which optimizes how data subsets are selected, formatted, and used for drug design. This specialized know-how, refined through the use of various proprietary algorithms and node control, delivers insights far beyond simple data aggregation, offering a consultancy-level advantage that’s challenging to in-source.”
- Dr Jonathan Hall, CEO & Co-Founder
We don’t just believe in Federated AI, we’ve built it, tested it, and proven it in the field. With managed infrastructure, secure server provisioning, and a software portal designed to enforce both performance and privacy, Qubigen’s Federated AI process is fast, secure, and cost-effective.
At its core, our approach isn’t just about having more data or even better data, it’s about having the right knowledge to unlock the full value of that data. And that’s what Qubigen delivers.
Qubigen - accelerate drug design without exposing secrets
Whether you're advancing active programs, reviving dormant data, or starting from scratch, Qubigen’s secure AI platform and virtual screening capabilities can help you identify, optimize, and accelerate the path to promising lead drug candidates. Get in touch to explore how we can support your next development.
References
[1] Madi, A., et al., A Secure Federated Learning framework using Homomorphic Encryption and Verifiable Computing. In 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS), 2021, pp. 1–8. https://cea.hal.science/cea-04558737/document
[2] Nguyen, T.V., Dakka, M.A., Diakiw, S.M. et al. A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Sci Rep 12, 8888 (2022). https://doi.org/10.1038/s41598-022-12833-x
[3] Hall, J.M.M., Nguyen, T.V., Dinsmore, A.W. et al. Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images. Reprod. BioMed. Online, 49, 6, 104403 (2024). 10.1016/j.rbmo.2024.104403