All Competitors

Protein-ligand binding affinity prediction from sequence and SMILES, without MSAs. Coarse-grained cofolding runs over 10x faster than Boltz-2.

Protein language model pretrained on structural domain segments, encoding fold and contact signals for remote-homology detection from sequence alone.

m6A RNA modification site prediction across the transcriptome, using a CNN-Transformer hybrid to surface unannotated N6-methyladenosine sites.

All-atom foundation model for macrocyclic peptide structure prediction, permeability estimation, and de novo design across non-canonical chemistries.

Variable-length generative protein design across structure, sequence, motif scaffolding, and peptide co-design via a generalized Poisson flow.

Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.

Generative language model that designs drug-like SMILES conditioned on disease ontology and a target protein sequence for de novo drug discovery.

All-atom foundation model for immune-receptor design that predicts structures and co-designs CDR sequences for antibodies, nanobodies, and TCRs.

Spatial multi-omics integration model aligning RNA, protein, metabolomics, and histology to map cell-state gradients and cell-cell interactions.

Hierarchy-aware self-supervised model for single-cell microscopy that preserves morphological structure suppressed by imaging-modality confounders.

Open-source all-atom co-folding foundation model for protein-ligand, protein-protein, and antibody-antigen complex prediction in drug discovery.

T-cell receptor-MHC restriction prediction from amino acid sequence, mapping TCRs to their restricting HLA allele at 0.97 held-out AUC.

Single-cell perturbation-response model predicting transcriptomic and cell-number changes for unseen perturbations plus inverse design.

Structure-based drug design language model fusing protein structural and evolutionary encoders with SAFE fragment tokens for hit-to-lead generation.

Spatial transcriptomics prediction from histology using conditional neural fields to reconstruct continuous gene expression fields.

Vision-language model that reads molecular structure images, translating them to SMILES, captions, and properties via chemical-bond topology.

RNA modification profiling from nanopore direct RNA-seq signal; self-supervised pretraining resolves 11 modification types and extends to new ones.

Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.

Brain MRI morphometry model that estimates cortical thickness, surface area, and volume in milliseconds instead of hours.

Heterograph foundation model for metabolic engineering, fusing protein, chemistry, and text embeddings over a 6.9M-node biological knowledge graph.

Atomic protein model building from cryo-EM density maps, resolving conformational heterogeneity through atom-centric sampling and diffusion.

Diffusion model for 3D small-molecule design against protein-protein interaction sites, guided by the natural binding peptide or protein partner.

Histopathology-to-molecular alignment model that queries H&E slides with gene-set signatures to predict pathway activity without sequencing.

Atomic-level refinement of RNA 3D structures, using geometric attention networks to guide physics-based Monte Carlo sampling and L-BFGS optimization.