Research Areas

Our vision is a future where biological research is conducted in continuous collaboration with intelligent systems, where AI does not just analyze data, but designs experiments, proposes mechanisms to prioritize biological experimentation, and helps engineer novel therapeutic concepts. Aithyra aspires to become a globally recognized hub for AI-driven life science, leading a new paradigm in which fundamental structural and functional principles of macromolecules and biological circuits and systems are learned and captured via cutting edge AI methods. In order to create sufficient amounts of data to support AI applications, we will invest in experimental automation through a robotics laboratory infrastructure. Such models will inform targeted experimental validation, thereby shortening the path to scientific insights, validating mechanistic hypotheses and the innovation of new medicines. 

AITHYRA is founded at this historical moment of transition, where traditional biology is constrained by complexity and computational science by formalism. We believe that AI offers a new kind of lens, one that can recognize patterns too vast or subtle for human intuition and derive working models without requiring explicit laws. This shift does not replace the scientific method but reframes it: hypotheses can now be generated from data, tested at scale by automated robots, and refined in a closed loop with intelligent systems. While an exciting opportunity, the new paradigm also brings new challenges, including model interpretability, data quality, and the risk of overfitting to systems that are not fully elucidated. 

While this research program is broadly applicable in life sciences, our vision is to focus on up to four disease areas: cancer, immunology, infectious diseases, and neurodegenerative disorders, which are physiologically interwoven but are also connected by features such as a richness of available data, underexplored complexity and a clear biomedical need. We will seek to bridge the gap between fundamental discovery and translational impact, applying the concept of programmable biology to decipher mechanisms (“program to understand”) and innovate therapies (“program to cure”). 

Scaling multi-modal data with an AI-driven robotics platform: To realize this paradigm, Aithyra will build a modular, AI-integrated robotics platform designed to serve as a general-purpose engine to understand biology across molecular, mesoscopic, cellular, and tissue scales. Modules will be connected through a centralized computational system that integrates AI-guided hypothesis generation, experimental scheduling, real-time data capture, and model retraining across the platform. By interlinking these lab-in-the-loop architectures, Aithyra’s robotics platform will enable a new class of closed-loop science—where machines generate and refine biological knowledge at scale, with minimal human supervision. Aithyra’s long-term ambition is to automate scientific discovery with the help of AI. 

Building and maintaining the AI-driven robotics platform: To support the construction, maintenance and continuous improvement of Aithyra’s AI-driven robotics platform, Wali Malik, an experienced Head of Lab Automation, will oversee operations and build and manage a team of 5-7 members with a diverse background in automation and software development. The development of the platform will happen in two stages. In Phase I, an initial platform will be built using ~100 m2 lab space in the Marxbox. Full platform development will occur in Phase II in Aithyra’s purpose-build research building where 400 m2 dedicated lab space.

We now live in an era when, for the first time in the history of science, hypothesis generation can be delegated to a machine. Recent examples show the first success of AI systems in tasks that typically require human creativity and logic, such as theorem proving and competitive programming. Adding to the previous success of AI in traditionally human creative tasks such as creative writing and art, these breakthroughs signal a profound change: machines are no longer just tools to implement human ideas but active participants and partners in generating new knowledge.

As Aithyra develops increasingly sophisticated AI/ML models across biological scales, the need for interpretable AI becomes strategically central. Many of the most impactful collaborations in the life sciences, particularly with experimentalists and clinicians outside of Aithyra, hinge not only on model performance but on the ability to understand and trust how predictions are made. Interpretable models leveraging learning techniques on knowledge graphs, causal learning, neuro-symbolic approaches, physics-inspired neural networks, or post hoc explanation techniques can serve as critical bridges between data-driven inference and mechanistic biological insight. To maximize the likelihood of early impact and sustained differentiation, Aithyra is strategically deprioritizing a number of otherwise important technologies and research areas that are currently less aligned with the AI-native model of scientific discovery.