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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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ProtBFN was developed to address this challenge. ProtBFN is a 650-million-parameter Bayesian Flow Network (BFN) trained on a curated dataset of 72 million biologically validated examples, optimised for generating new protein sequences.

Exploring the Proteome with ProtBFN...

on Mar 06, 2025 | 11:59am

Proteins are essential to life, driving nearly every biological process and performing critical functions in the human body—from building muscles to fighting diseases. Understan...

News
The AI Action Summit was an event that brought together world leaders, business experts and research luminaries as they uncovered the path ahead for the development of AI around the globe, and with the InstaDeep team being in the thick of the action throughout. Join us below as we share some of the highlights.

InstaDeep at the AI Action Summit, Grand Palais, Paris...

on Feb 20, 2025 | 11:45am

The AI Action Summit was an event that brought together world leaders, business experts and  research luminaries as they uncovered the path ahead for the development of AI around...

Blog
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...

Blog

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
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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.
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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).
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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.
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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.
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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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