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 »
BioNTech hosted its annual AI Day at the Science Museum in London, the second event in their Innovation Series. The day brought together investors, analysts, media representatives and guests to explore how artificial intelligence could transform science and medicine. As part of BioNTech’s AI ecosystem, InstaDeep showcased the progress made across multiple fronts, from advanced… Read more »
Reinforcement learning (RL) is increasingly being applied to complex processes across science and engineering, with promising results in manufacturing, biology, and energy systems. By learning through trial and error, RL agents can optimise behaviour without explicit supervision1. Many of these processes are governed by differential-algebraic equations (DAEs). These combine time-dependent dynamics with algebraic constraints, making… 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 »
Each year, the Genetic and Evolutionary Computation Conference (GECCO) gathers the leading global experts in the domain of genetic and evolutionary computing. This year, InstaDeep’s research team presented two main conference papers and one workshop paper at the event, which took place in Boston, MA between 9-13 July. The three works result from a close… Read more »
BOSTON (MAY 19, 2022) – InstaDeep, a leader in advanced AI decision-making systems for solving real-world problems, announced Thursday it has opened its first US office in the heart of the dynamic Cambridge, MA, life sciences, university research and innovation hub. The new R&D base for InstaDeep will allow the AI company to work with… Read more »
InstaDeep is pleased to announce the formation of a new Quantum Machine Learning (QML) team within the company’s well-established research department. The announcement coincides with the publication of the “Autoregressive neural-network wavefunctions for ab initio quantum chemistry” research paper in the leading science journal, Nature Machine Intelligence. Publication in Nature Machine Intelligence The paper was… Read more »
InstaDeep is delighted to announce that a collaborative research project with a team from the University of Oxford on “Autoregressive neural-network wavefunctions for ab initio quantum chemistry” has been published in Nature Machine Intelligence magazine. The paper was authored by Dr Thomas Barrett of InstaDeep as well as Prof A. I. Lvovsky and Aleksei Malyshev… Read more »
LONDON (March 22, 2022) InstaDeep today announced it will use the NVIDIA Cambridge-1 supercomputer to accelerate the next wave of biology innovation. The company plans to train AI language models using genomics data, and these models will then be shared with the bioinformatics community. InstaDeep’s work on machine learning and bioinformatics has already led to… Read more »