All Competitors

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

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.

Molecular foundation model that turns SMILES into 2048-dimensional embeddings from multiple 3D conformations for ADMET and virtual screening.

Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.

Hallucination-based multichain protein design framework that co-folds chains and forms the inter-chain interfaces of dimers, trimers, and tetramers.

Supervised variational autoencoder that learns a tissue-aware latent space for bulk RNA-seq, trained on harmonized TCGA, GTEx, and ARCHS4 data.

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

Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.

Vision-guided model that builds virtual 3D organoid surrogates from brightfield microscopy to predict chemical perturbation responses without omics.

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

Self-supervised 3D masked autoencoder for volumetric fluorescence microscopy, aligned to ESM2 embeddings to predict protein localization.

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.

Small-molecule hit-discovery pipeline using Boltz-2 co-folding and affinity prediction to rank in-stock compounds or make-on-demand chemical space.

De novo protein binder and nanobody design pipeline that ranks candidates by a protein-protein interaction model rather than structural confidence.

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