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.

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

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

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

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

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

Text-guided localization model that grounds natural-language functional descriptions to specific residue regions of a protein sequence.

Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.

Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.

Multimodal molecular generation model for drug design, conditioned on properties, pharmacophores, protein sequences, or protein binding pockets.

Multi-view single-cell foundation model at 12B parameters, aligning expression and perception views with an LLM-JEPA joint-embedding objective.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Self-supervised foundation model for clinical flow cytometry, producing panel-agnostic specimen-level representations from multi-panel data.

Diffusion model that generates 3D small molecules conditioned on protein pockets and partial fragments encoded as continuous spatial density maps.

Zero-shot generative framework that turns 3D pharmacophores into synthesis-ready DNA-encoded libraries of purchasable building blocks.

Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.

Autoregressive generative model that uses reinforcement learning to optimize mRNA codon sequences for MFE, CAI, and GC content.

NMR foundation model trained on 158 million simulated 1H and 13C spectra, transferring simulation-learned representations to real experimental data.

Ab initio gene annotation model that predicts gene boundaries and exon-intron structure from raw DNA, generalizing zero-shot to unseen species.

Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.

Toxicity screening foundation model that encodes Tox21 concentration-response curves and assay metadata into reusable 768-dimensional embeddings.

Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.