We ask what machine-learned representations can reveal about biology (and where they fail), then use those answers to build more reliable tools for discovery and protein engineering.

By doing so, we develop quantitative and machine-learning methods to understand, evaluate, and engineer biology.

Our work spans scalable biological inference, rigorous evaluation of predictive models, and generative design of proteins and molecular interactions.

We pair large-scale computation with careful benchmarking and experimental collaboration to identify what models can reliably tell us about biology.

Current research

Protein complexes at proteome scale

We build and analyse large-scale predictions of protein complexes to extend structural biology beyond individual chains, enabling discovery of interaction interfaces, complex-dependent folds, and conserved structural patterns across the tree of life.

Find out more https://doi.org/10.64898/2026.03.27.714458

Generative protein and binder design

We develop generative methods for atomistic protein and binder design, and study how pretraining, inference-time optimization, and experimental evaluation can improve specificity and usable hit rates.

Find out more https://arxiv.org/abs/2603.27950

Foundational contributions

Protein Transformers

In ProtTrans, we trained large AI models to read protein sequences like a language, similar to how modern AI reads human text. These models learned fundamental principles about how proteins work without being explicitly taught biology, chemistry or physics. Most remarkably, our best model (ProtT5) achieved state-of-the-art performance in predicting protein properties while being dramatically faster than traditional methods.

Find out more https://doi.org/10.1109/TPAMI.2021.3095381

Nucleotide Transformers

We contributed Nucleotide Transformer (NT), a deep learning system that learned multiple genomes from humans and other species. NT learned the language of DNA by unmasking millions of genetic sequences. Through this process, it discoverd patterns in DNA that control how genes work, where genes begin and end, and how DNA variations might affect human health.

Find out more https://doi.org/10.1038/s41592-024-02523-z

Also check out GenSLMs, a generative model for genome sequences: https://doi.org/10.1177/10943420231201154

Efficient homology search

In this project, we tackled fundamental computational bottlenecks in protein sequence comparison by redesigning core algorithms to efficiently utilize modern GPU architectures. The work combines elements of parallel algorithm design, high-performance computing, and computational biology. With our collaborators, we proposed novel GPU-optimized algorithms for both gapless sequence filtering and gapped alignment using protein profiles, achieving speeds up to 100 trillion cell updates per second. Beyond raw performance gains, we focused on making these advanced search capabilities accessible even on low power (W) GPUs, enabling broader adoption in the research community.

Find out more https://doi.org/10.1038/s41592-025-02819-8

Real-world evaluation for protein engineering

We develop benchmarks that test whether fitness-prediction models generalize under the distribution shifts that arise in real protein-engineering campaigns—not merely on random train/test splits.

FLIP: https://openreview.net/forum?id=p2dMLEwL8tF

FLIP2 (ICML 2026 oral): https://flip.protein.properties/

Predictions at your fingertips

PredictProtein represented a pioneering effort in making protein analysis accessible to scientists worldwide. Since 1992, it helped researchers understand proteins by predicting their structure and function from amino acid sequences. We contributed the latest update to PredictProtein, enhancing it with predictions from protein language models, the first significant departure from explit use of evolutionary information.

Find out more https://doi.org/10.1093/nar/gkab354