Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 2185–2208 of 2335 models
Multimodal pathology assistant that answers questions about histology and cytology images, pairing the PathCLIP vision encoder with a Vicuna-13B LLM.
Spatial proteomics imputation from a 7-plex immunofluorescence panel, generating in silico CODEX expression for 33 more biomarkers per cell.
Protein model quality assessment predicting per-residue lDDT for monomer and multimer interface models from graph-coupled ESM embeddings.
Multi-modal LLM answering free-form questions about a compound's indications, pharmacodynamics and mechanism of action from its SMILES string.
Vision-language framework for 3D medical image diagnosis and visual question answering, bridging frozen image encoders and LLMs, shown on brain MRI.
Generative medical visual question answering model that pairs a vision encoder with a language model, trained on the 227k-pair PMC-VQA dataset.
Multi-domain protein and complex assembly from deep-learned inter-domain interactions, averaging TM-score 0.922 across 219 multi-domain targets.
Zero-shot antibody affinity maturation using ESM pseudolikelihood scoring. Improves binding up to 160-fold with no antigen-specific training data.
Single-cell foundation model pre-trained on 22 million transcriptomes, using rank-based gene encoding for clustering and trajectory inference.
Drug pair synergy prediction for rare cancer tissues, read from a language model's representation of a screening row written out as a sentence.
Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.
Rigid protein-protein docking by diffusion over the rigid-body pose, with a confidence model ranking sampled complexes. Median C-RMSD 4.85 on DIPS.
Protein structure encoder pretrained by contrastive alignment to a frozen protein language model, anchored by self-supervised contact-map prediction.
Biomedical vision-language model trained contrastively on 1.6M figure-caption pairs mined from PubMed Central open-access articles.
Joint sequence-structure protein representation framework that fuses ESM-2 language model embeddings with GearNet geometric graph neural networks.
Structure-based drug design by SE(3)-equivariant diffusion over 3D atom coordinates and types, with the same frozen network scoring binding affinity.
Remote-homolog template recognition that threads a sequence against clustered PDB and AlphaFold DB structures to improve AlphaFold2 modelling.
Biomedical vision-language model trained contrastively on 15M PubMed Central figure-caption pairs for zero-shot classification, retrieval, and VQA.
Self-supervised pretraining for 3D medical images that learns anatomical correspondences between scans, giving encoders transferable to segmentation.
AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.
Cyclic peptide structure prediction and de novo macrocycle design, by wrapping a frozen structure predictor's positional encoding into a ring.
Protein language model that annotates intrinsically disordered regions per residue from sequence alone, without MSAs or biophysical features.
Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.