Category: Machine Learning

Articles

InstaDeep’s latest innovation, Nucleotide Transformer v3 (NTv3), is a single framework that unites these once-separate capabilities. NTv3 learns representations, predicts functional readouts, annotates genomes and designs new sequences across multiple species. By reasoning across one million nucleotides at single-base resolution, it gains access to the long-range regulatory logic that shapes genomic function, moving the field from simply reading genes to engineering them.

Modelling the Genome with NTv3

The genome is not a linear string of information, but a long-range, three-dimensional system in which regulatory signals act across large genomic distances to control gene expression. Interpreting this requires models that can connect nucleotide-level sequence to long-range regulation, cell-state–specific expression programmes, and the phenotypic consequences of genetic variation. Meeting this challenge demands single-nucleotide resolution,… Read more »

Genome annotation with SegmentNT

Genome annotation with SegmentNT

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 »

Hand in Hand for Africa’s AI Future - InstaDeep at Deep Learning Indaba 2025

Hand in Hand for Africa’s AI Future – InstaDeep at Deep Learning Indaba 2025

Africa’s AI community gathers annually for the Deep Learning Indaba to exchange ideas, learn, and dream big. As InstaDeep we have supported this journey from the start, both through sponsorship including travel grants that help students from across Africa attend and by engaging actively in the community. This year in Kigali, we teamed together with… Read more »

Accelerate molecular simulations with mlip

Accelerate molecular simulations with mlip

Understanding molecular behaviour allows researchers to predict the physical and chemical properties of complex systems1, such as how a protein folds or how a drug binds to its target. These insights are critical across biology, chemistry, and materials science2, especially when experiments are costly, time-consuming, or difficult to scale.  Yet molecular science has long grappled… Read more »