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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New heights - InstaGeo at the AI Action Summit 2025

New heights – InstaGeo at the AI Action Summit 2025...

on Feb 10, 2025 | 05:04pm

Learn how the InstaGeo team led the way in AI for Social Good at the AI Action Summit in Paris, where their work is gaining recognition for its tangible impact. As we step...

Building the Future of Food Security - GeoAI Hack

Building the Future of Food Security – GeoAI Hack...

on Feb 07, 2025 | 03:48pm

Bringing together over 100 participants from around the world in teams building novel AI-powered geospatial tools to solve real-world challenges in climate adaptation. The...

AI Action Summit - Coming Together to Build the Future of AI

AI Action Summit – Coming Together to Build the Future of A...

on Feb 04, 2025 | 10:47am

AI is evolving at an unprecedented rate. Worldwide, leaders are striving to grasp the implications of this transformative technology while ensuring its economic and societal benef...

The GeoAI Hackathon on 4-5 February 2025, co-organised by InstaDeep and datacraft, invites AI enthusiasts, developers, and innovators to create solutions that harness the transformative power of artificial intelligence and geospatial data.

GeoAI Hackathon: Tackling Africa’s Locust Crisis with AI Innova...

on Jan 23, 2025 | 01:09pm

Desert locust swarms represent one of the greatest threats to food security in Africa. Capable of consuming their weight in crops daily, these pests devastate livelihoods, destroy...

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...

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

BulkRNABert: Cancer prognosis from bulk RNA-seq based language models

Maxence Gélard | Guillaume Richard | Thomas Pierrot | Paul-Henry Cournède

ML4H 2024 Dec 2024
BulkRNABert pipeline. The 1st phase consists in pre-training the language model through masked language modeling using binned gene expressions. The 2nd phase fine-tunes a task-specific head using either cross-entropy for the classification task or a Cox-based loss for the survival task. IA3 rescaling is further added for the classification task.

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.

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