All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–14 of 14 filtered models
ATOMICA
—3—Geometric deep learning model that learns atomic-scale representations of molecular interfaces across proteins, small molecules, and nucleic acids.
ProteinSmall moleculeRNA88OpennessMolChord
—2—Structure-based drug design model that generates ligands for a protein pocket, pairing a diffusion structure encoder with preference optimization.
Small moleculeProtein23OpennessPairMixer
334—Genesis Therapeutics +1 otherOctober 21, 2025molecular_dockingprotein_designrepresentation_learning+3Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.
ProteinSmall molecule77OpennessPUMBA
—1—Florida International UniversityOctober 19, 2025protein_protein_interactionrepresentation_learningstate_space_model+2Protein-protein docking scorer that ranks interface poses from image-encoded patches, swapping PIsToN's Vision Transformer for Vision Mamba.
Protein20OpennessMatcha
325—Molecular docking model that predicts protein-ligand binding poses with multi-stage Riemannian flow matching, yielding physically valid geometry.
Small moleculeProtein23OpennessFoldMatch
11——Protein structure embedding model that compresses each 3D fold into a single fixed-length vector for proteome-wide similarity search and clustering.
Protein23OpennessCryo-ET particle picking model that averages tiny, medium, and large 3D U-Nets pretrained on simulated tomograms and fine-tuned on experimental data.
Imaging86OpennessMonjuDetectHM
211—Cryo-ET particle picking ensemble of three 3D segmentation models predicting particle-center heatmaps with ResNet50d and EfficientNetV2-M backbones.
Imaging95OpennessTopCUP
3——Cryo-ET particle picking model, an ensemble of 3D U-Nets with EfficientNet encoders that finds protein complexes in tomograms by heatmap segmentation.
Imaging96OpennessBPD
2——Cryo-ET particle picking model that localizes six protein complexes in tomograms using an ensemble of lightweight 3D U-Nets.
Imaging67OpennessOctopi
13——Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.
Imaging81OpennessSABER
18——Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.
Imaging78OpennessCryoLens
19——Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.
Imaging74OpennessCryo-IEF
73——Cryo-EM foundation model pre-trained on 65 million particle images, enabling zero-shot classification, pose clustering, and quality assessment.
Imaging42Openness