InstaDeep

Multi-species genomics foundation model spanning representation learning, functional-track prediction, and sequence generation at 1 Mb context.

Bimodal masked language model that jointly encodes bulk RNA-seq expression and DNA methylation into patient-level embeddings for cancer genomics.

Antibody foundation model generating paired sequences with genetic and developability metadata for labelling, humanisation, and library design.

Protein sequence representation model pairing a sequence autoencoder with a denoising diffusion model over its latent space for frozen embeddings.

Transferable coarse-grained force field for molecular dynamics of proteins, RNA, and lipids, built on the MACE equivariant graph architecture.

Bulk RNA-seq foundation model that learns patient-level embeddings from binned gene expression for pan-cancer classification and survival prediction.

Surrogate machine learning force field reusing a reference model's node features from earlier steps, running molecular dynamics eight times faster.

Protein fitness prediction model meta-trained across ProteinGym deep mutational scans, transferring to unseen assays in context with no task data.

Multimodal model fusing pre-trained DNA, RNA, and protein encoders through cross-attention to predict tissue-specific transcript isoform expression.

Conversational agent that answers plain-language questions about DNA, RNA, and protein sequences by coupling a genomics encoder to a language model.

Genome annotation model that labels 14 genic and regulatory element classes at single-nucleotide resolution across DNA windows up to 50 kb.

DNA foundation models from 500M to 2.5B parameters, trained on 3,200+ human genomes and 850 species for variant effect prediction.