Decision-Making AI For The Enterprise

InstaDeep delivers AI-powered decision-making systems for the Enterprise. With expertise in both machine intelligence research and concrete business deployments, we provide a competitive advantage to our customers in an AI-first world.

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Building AI systems for the industry

Leveraging its expertise in GPU-accelerated computing, deep learning and reinforcement learning, InstaDeep has built AI systems to tackle the most complex challenges across a range of industries and sectors.

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Our latest updates from across our channels

Celebrating Breakthroughs: Dr Andrija Sente Honoured During Nobel...

on Dec 16, 2024 | 10:27am

Each year in Stockholm, Nobel Prize Week celebrates the ideas and breakthroughs that have transformed our understanding of the world. In the same week (6–12 December), the Scien...

InstaDeep presents 8 papers at NeurIPS 2024

InstaDeep showcases 8 papers at NeurIPS 2024...

on Dec 09, 2024 | 03:16pm

From our beginnings to becoming a key player in AI, supporting new research spaces and sharing what we’ve learnt in AI has always been at the core of InstaDeep’s DNA. This...

Decoding our Genome with Nucleotide Transformers...

on Dec 05, 2024 | 12:06pm

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 massi...

Karim Beguir addresses G7 – presenting vision for AI and su...

on Nov 14, 2024 | 04:00pm

In October, InstaDeep’s Co-Founder and CEO, Karim Beguir, was asked to contribute to a G7 Ministerial Meeting on Development, hosted by current Presidents, Italy. His message wa...

InstaDeep unveils near-exascale supercomputer ‘Kyber,’...

on Oct 24, 2024 | 02:24pm

Kyber increases InstaDeep’s compute power 10x, facilitating AI innovation.Powered by NVIDIA H100 GPUs, with ~0.5 exaFLOPs in FP16, ranking among the top 100 global AI clusters.I...

AI Day 2024: InstaDeep Showcases Innovations in Biology and AI as Part of the BioNTech Innovation Series

AI Day 2024: InstaDeep Showcases Innovations in Biology and AI as...

on Oct 09, 2024 | 10:40am

On 1st October 2024, over 100 members of the biotech community – including scientists, researchers, engineers and journalists – gathered at London’s tech hub, CodeNode, for...

AI Day

BioNTech Highlights AI Capabilities and R&D Use Cases at Ina...

on Oct 01, 2024 | 01:00pm

Provides updates on BioNTech’s strategy to scale and deploy AI-capabilities across the immunotherapy pipeline Highlights InstaDeep’s new near exa...

Fireside Chat on the Future of AI: Insights from Karim Beguir and...

on Sep 18, 2024 | 04:24pm

InstaDeep Co-Founder and CEO Karim Beguir returned to his alma mater to share the stage at New York University with Meta’s Chief Scientist Yann LeCun. In a session titled “...

Sharing knowledge and accelerating innovation at Deep Learning In...

on Sep 11, 2024 | 05:56pm

Amid the vibrant energy of the Deep Learning Indaba 2024, InstaDeep proudly reinforced its commitment to advancing AI innovation across Africa by supporting a new edition of this...

InstaDeep introduces DeepPCB Pro: An AI-powered PCB design tool...

on | 03:50pm

San Francisco, CA – 12th September 2024 – InstaDeep, in collaboration with Google Cloud, unveiled today the advanced version of its AI-powered Printed Circuit Board (PCB) desi...

Research

Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean

Carolin Benjamins | Shikha Surana | Oliver Bent | Marius Lindauer | Paul Duckworth

NeurIPS 2024 workshop Dec 2024
Bayes Opt for Protein Design

BoostMD – Accelerating MD with MLIP

Lars L. Schaaf | Ilyes Batatia | Christoph Brunken | Thomas D. Barrett | Jules Tilly

NeurIPS 2024 workshop Dec 2024
Free energy surface of unseen alanine-dipeptide Comparison of the samples obtained by running ground truth MD and boostMD. The free energy of the Ramachandran plot, is directly related to the marginalized Boltzmann distribution exp [−F(ϕ, ψ)/kBT]. The reference model is evaluated every 10 steps. Both simulations are run for 5 ns (5 × 106 steps).

Learning the Language of Protein Structures

Benoit Gaujac | Jérémie Donà | Liviu Copoiu | Timothy Atkinson | Thomas Pierrot | Thomas D. Barrett

NeurIPS 2024 workshop Dec 2024
Schematic overview of our approach. The protein structure is first encoded as a graph to extract features from using a GNN. This embedding is then quantized before being fed to the decoder to estimate the positions of all backbone atoms.

Metalic: Meta-Learning In-Context with Protein Large Language Models

Jacob Beck | Shikha Surana | Manus McAuliffe | Oliver Bent | Thomas D. Barrett | Juan Jose Garau Luis | Paul Duckwort

NeurIPS 2024 workshop Dec 2024
We introduce Metalic, an in-context meta-learning approach for protein fitness prediction in extreme low-data settings. Critically, Metalic leverages a meta-training phase over a distribution of related fitness prediction tasks to learn how to utilize in-context sequences with protein language models (PLMs) and generalize effectively to new fitness prediction tasks. Along with fine-tuning at inference time, Metalic achieves strong performance in protein fitness prediction benchmarks, setting a new SOTA on ProteinGym, with significantly fewer parameters than baselines. Importantly, Metalic demonstrates the ability to make use of in-context learning for zero-shot tasks, further enhancing its applicability to scenarios with minimal labeled data.

Bayesian Optimisation for Protein Sequence Design: Back to Basics with Gaussian Process Surrogates

Carolin Benjamins | Shikha Surana | Oliver Bent | Marius Lindauer | Paul Duckworth

NeurIPS 2024 workshop Dec 2024
: Multi-round design averaged over eight single-mutant protein landscapes. Left: Top-30% recall (mean and 95%-CI). Our methods are highlighted with ∗ . Right: Wall-clock runtime interpreted across hardware as compute costs. Our GP with string (SSK) or fingerprint (Forbes) kernels are competitive with PLM baselines whilst only requiring a fraction of runtime and no pre-training.

Multi-modal Transfer Learning between Biological Foundation Models

Juan Jose Garau-Luis | Patrick Bordes | Liam Gonzalez | Masa Roller | Bernardo P. de Almeida | Lorenz Hexemer | Christopher Blum | Stefan Laurent | Jan Grzegorzewski | Maren Lang | Thomas Pierrot | Guillaume Richard

NeurIPS 2024 Dec 2024
We demonstrate IsoFormer’s capabilities by applying it to the largely unsolved problem of predicting how multiple RNA transcript isoforms originate from the same gene (i.e. same DNA sequence) and map to different transcription expression levels across various human tissues.

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