All Competitors

Temporal diffusion framework for single-cell developmental dynamics, interpolating and forecasting cell states from irregularly sampled time series.

Learning-to-rank variant effect predictor that aligns overlapping deep mutational scanning assays into an assay-agnostic tolerance measure.

Multimodal deep learning model that predicts protein-mediated chromatin contact maps and loops de novo from protein-binding profiles and sequence.

Foundation model for 3D genome architecture, using masked locus modeling over genome-wide contact profiles to capture chromosome-scale organization.

Knowledge-graph-grounded model that predicts single-cell transcriptomic responses to small molecules, with zero-shot prediction for unprofiled drugs.

Longitudinal chest X-ray vision-language model that reads a follow-up radiograph against its prior study and forecasts how findings will change.

Gene Ontology function prediction fusing protein language model embeddings, domains, and interaction data through a learned weighting layer.

Knowledge-graph foundation model for drug repurposing, grounding a biomedical graph in cell-type-specific genetic associations to rank indications.

RNA subcellular localization predictor that fuses physicochemical interaction graphs with frozen RiNALMo embeddings via a gated fusion layer.

Plant genomic foundation models from 0.1B to 1B parameters, pretrained on 43 phylogenetically diverse plant genomes for variant effect prediction.

Polarizable machine-learning interatomic potential extending MACE with long-range electrostatics, trained on 100M OMol25 DFT calculations.

Diffusion model predicting single-cell responses to genetic or drug perturbations, generating over distributions to capture population variability.

Enzyme function prediction model that uses contrastive learning to assign the first three EC digits to enzymes with functions unseen during training.

Ultrasound foundation model pretrained on five million public frames, predicting masked latent features against a frozen domain-specific teacher.

Protein-ligand binding affinity scorer using an SE(3)-equivariant graph network trained on 741,706 co-folded complexes with target-disjoint splits.

Conditional variational autoencoder for antimicrobial peptide design that disentangles sequence, function, and length for independent control.

Prime editing efficiency prediction from pegRNA sequence, with every biochemical step of the editing mechanism modeled as its own learned rate.

Protein evolution model that learns indel dynamics and epistasis from unaligned sequences, simulating trajectories that yield functional proteins.

Healthcare reasoning model for symptom triage, wellness planning, and clinical workflows, post-trained with rubric-based reinforcement learning.

Single-cell foundation model applying discrete diffusion directly to scRNA-seq counts, generating unconditional and perturbation-conditioned profiles.

Genomic foundation model training framework whose joint-embedding predictive objective learns functional representations of masked DNA, not tokens.

BERT-style language model for somatic mutations, pretrained on cancer sequencing from 210,000+ patients for tumor subtyping and therapy response.

Retrieval-augmented framework for de novo peptide binder design that conditions generation on retrieved, structurally aligned binding evidence.