University College London

Protein language model pretrained on structural domain segments, encoding fold and contact signals for remote-homology detection from sequence alone.

Promptable DNA language model that generates multi-kilobase plasmid sequences from plain-language component specs, refined with verifiable rewards.

Protein function prediction model that fuses sequence, structure, text, and interaction embeddings with learned gating to assign Gene Ontology terms.

Protein-family language model trained on unaligned homolog sets for zero-shot variant fitness prediction and design. ProFam-1 holds 251M parameters.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Brain foundation model unifying EEG and MEG in a single encoder via a shared discrete tokenizer that transfers across sensor layouts and montages.

Self-supervised foundation model for retinal imaging, pretrained on 1.6 million unlabelled fundus and OCT scans to detect ocular and systemic disease.