Combinatorial optimisation (CO) problems are among the hardest challenges in computer science, with real-world importance in logistics, transport, and energy systems. These problems involve choosing the “best” option from an astronomically large set of possibilities and are often NP-hard, meaning they become computationally intractable as the problem size grows. Neural solvers, advanced AI approaches designed… Read more »
Reinforcement learning (RL) has delivered some of AI’s most striking successes, from human-level Atari 1 play to world-class performance in Go2. Yet when applied to messy, real-world combinatorial optimisation (CO) problems such as energy grid management or autonomous logistics, even state-of-the-art RL systems can stall. Despite being trained to convergence, policies often hit a performance… Read more »
Multi-agent reinforcement learning (MARL) holds significant promise across domains such as autonomous driving, warehouse logistics, intelligent rail networks, and satellite alignment. Yet deploying MARL in the real world remains difficult. Training typically requires vast amounts of interactive data, which is both costly and potentially risky, particularly in safety-critical settings where trial and error is not… Read more »
Nucleotides are the fundamental units of DNA, and when linked together by a sugar-phosphate backbone, they form the strands that define our genome. Analysing the precise role of each nucleotide within these sequences is essential to understanding their influence on gene regulation and disease. However, the human genome contains around 3 billion nucleotides in a… Read more »
By Alex Graves The story of Bayesian Flow Networks (BFNs) took years to take shape. In 2010, I was working with autoregressive models and repeatedly ran into the same constraint: their fixed prediction order, in which a single variable is completely revealed at every step. This rigid structure felt unnatural compared to how we typically… Read more »
As AI continues to reshape our understanding of the biological landscape, DNA, RNA, and protein sequence models are rapidly emerging—each promising to be faster, more capable, and more accurate than the last. While genomic data is being generated at an astonishing pace, making sense of it remains a challenge. A key barrier is usability. Most… Read more »
Antibodies play an important role in the adaptive immune response. By selectively recognising and binding to specific antigens—such as viruses or bacteria—they neutralise threats and are essential to both acute and long-term immunity. This ability to target a wide range of molecules has made antibodies indispensable to therapeutic development, fueling a market valued at $252.6… Read more »
AI is revolutionising proteomics and has the potential to unlock new frontiers in targeted healthcare and biomedical research. At the heart of this is peptide sequencing, an essential process for understanding proteins and their role in biological systems. Why Peptides? Peptides are the building blocks of proteins. Accurately sequencing them drives advancements in drug discovery,… Read more »
Proteins are essential to life, driving nearly every biological process and performing critical functions in the human body—from building muscles to fighting diseases. Understanding these intricate molecules has long challenged researchers, but advancements in AI could change that. What is ProtBFN? There are 20 proteinogenic amino acids that make up proteins. Arranged in specific sequences,… Read more »
DNA is the blueprint of life, the universal code of A, T, C, and G that guides the functioning of all living organisms, from humans to bacteria. Packed into the genome, this massive instruction manual varies in complexity across species and contains billions of letters that determine traits, health, and evolution. Small variations in DNA… Read more »