Pietro Liò
University of Cambridge, UK

Title: Designing biological molecules with generative AI: from RNA structure to synthesisable drugs

Abstract: Recent advances in generative artificial intelligence are beginning to transform how biological molecules are designed. Across RNA engineering, protein representation learning, and drug discovery, new models can now generate sequences and structures that are not only novel but also experimentally viable. For example, structure-conditioned RNA language models can design complex RNA folds and catalytic molecules that match or exceed human expert performance in laboratory validation, enabling the automated creation of functional ribozymes and structured RNAs. In parallel, synthesis-aware generative frameworks for small molecules incorporate chemical reactions directly into the generation process, producing drug-like compounds together with feasible synthetic routes rather than purely theoretical structures. Complementary work on large-scale protein representation learning shows that training on massive structural datasets improves the ability of models to capture relationships between sequence, structure, and function, providing foundations for data-driven biomolecular design. Together, these studies highlight a shift toward integrated AI pipelines that couple generative design with structural reasoning and experimental validation, moving the field closer to programmable biomolecules and practical AI-driven biotechnology.

Bio: Pietro Liò is a Professor of Computational Biology at the University of Cambridge and a member of Clare Hall. His research lies at the intersection of machine learning, network science, and biology, with a strong focus on understanding complex biological systems such as gene regulation, disease mechanisms, and aging. He has been a pioneer in applying graph-based methods, including graph neural networks, to biomedical data, enabling the integration of heterogeneous information across molecular, cellular, and clinical scales. His work spans topics such as systems biology, precision medicine, and the modeling of chronic diseases, often combining mathematical rigor with data-driven approaches.

Aidong Zhang
University of Virginia, USA

Title: Interpretability for Responsible Medical AI

Abstract: In recent years, major advances in artificial intelligence (AI) have been applied to medical and health data with promising results. Even though these methods demonstrate incredible potential in saving valuable man-hours and minimizing inadvertent human mistakes, their adoption has been met with rightful skepticism and extreme circumspection in critical applications such as medical diagnosis. The most paramount of these challenges is the lack of rationale behind predictions - making them notoriously a black box in nature. In extreme cases, this can create a lack of alignment between the designer's intended behavior and the model's actual performance. In this talk, I will discuss our recent research on explainable AI strategies, in particular, I will discuss concept-based learning models and show how the concept-based learning models and example-based learning models can be designed for explainable deep neural networks, vision transformers, and vision language models.

Bio: Dr. Aidong Zhang is Thomas M. Linville Endowed Professor of Computer Science in the School of Engineering and Applied Sciences at University of Virginia (UVA). She also holds joint appointments with Department of Biomedical Engineering and School of Data Science at University of Virginia. Her research interests include machine learning, data mining, bioinformatics, and health informatics. Dr. Zhang is a fellow of ACM (Association for Computing Machinery), AIMBE (American Institute for Medical and Biological Engineering), and IEEE (Institute of Electrical and Electronics Engineers). She is also a member of the Virginia Academy of Science, Engineering and Medicine (VASEM).

Tom Pollard
Massachussetts Institute of Technology, USA