Helmholtz Munich
A German biomedical research center pursuing better health in a changing environment, spanning diabetes, lung disease, and computational health.
Models (14)
Hierarchy-aware self-supervised model for single-cell microscopy that preserves morphological structure suppressed by imaging-modality confounders.
Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.
Self-supervised 3D masked autoencoder for volumetric fluorescence microscopy, aligned to ESM2 embeddings to predict protein localization.
Genetically aligned foundation model for blood smear cytology that links single-cell morphology to the chromosomal aberrations behind AML and APL.
Virtual spatial transcriptomics foundation model predicting pan-cancer, spatially-resolved single-cell gene expression from H&E histology slides.
Protein language model that predicts per-residue local energetic frustration directly from sequence, covering whole proteomes and disordered regions.
LoRA adapter on ProstT5 predicting per-residue distributions over Foldseek 3Di tokens, capturing conformational flexibility from MD trajectories.
Single-cell foundation model adapting LLaMA-3.1-8B with LoRA, recasting transcriptomes and protein interaction networks as natural-language Q&A pairs.
Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.
Single-cell foundation model learning technology-agnostic cell embeddings by contrasting cell views rather than reconstructing gene expression counts.
FLOWR.root
Pfizer / Jagiellonian University Medical College / Helmholtz Munich / Technical University of Munich
Released October 2, 2025
SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.
Scooby
Technical University of Munich / Helmholtz Munich / Harvard Medical School / Broad Institute / Harvard University
Released October 1, 2025
Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.
TITAN
Mahmood Lab / Brigham and Women's Hospital / Helmholtz Munich / University of Tokyo
Released November 29, 2024
Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.
Transformer foundation model pretrained on 110M single-cell and spatial transcriptomics profiles, transferring spatial context to dissociated cells.