Structure-aware protein language model using structure-guided masking and a causal objective for variant effect prediction and protein discovery.
Hierarchical DNA foundation model that co-trains a dynamic token-merging tokenizer with latent Transformers to match genomic information density.
Biomap Research / Chinese University of Hong Kong / Mohamed bin Zayed University of Artificial Intelligence
Released September 2, 2025
Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.
DNA foundation model for germline variant pathogenicity, pretrained on 27 mammalian genomes and fine-tuned on ClinVar and HGMD for SNVs and indels.
Long-sequence DNA foundation model with groove-aware convolutions and reverse-complement gating over 100kb contexts. Averages 0.708 MCC on GUE.
Multi-modal protein language model using the MSA evolutionary profile as a reasoning step between structure and sequence. 650M outperforms ESM-3 1.4B.
Westlake University / Zhejiang University / Biomap Research / The University of Hong Kong
Released February 11, 2025
Multi-omics foundation model that folds DNA, RNA, and protein into one codon-level nucleotide representation following the central dogma.
Princeton University / BioMap / Zhejiang University / Stanford University
Released December 31, 2024
RNA foundation model unifying sequence representation, 3D structure prediction, and de novo design. Ranks first on 11 of 13 BEACON tasks.
Shanghai Jiao Tong University / Fuzhou University / BioMap / Shandong University
Released September 13, 2024
Sequence-conditioned diffusion model that samples backbone conformational ensembles for intrinsically disordered proteins without MSAs or simulation.
BioMap / Tsinghua University / Mohamed bin Zayed University of Artificial Intelligence
Released June 9, 2024
Scaling-law study of protein language models identifying compute-optimal training for causal and masked objectives on 939 million protein sequences.
Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.
Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.
Asymmetric encoder-decoder transformer for single-cell RNA-seq that encodes only non-zero genes, cutting FLOPs 10-100x versus standard transformers.
Zhejiang University of Technology / BioMap / Mohamed bin Zayed University of Artificial Intelligence
Released May 18, 2023
Protein model quality assessment predicting per-residue lDDT for monomer and multimer interface models from graph-coupled ESM embeddings.