All Competitors

Tandem mass spectrometry model that embeds MS/MS spectra and molecular graphs in one space, ranking candidate structures without a spectral library.

Catalytic residue prediction from enzyme sequence alone, pairing frozen ESM2 embeddings with contrastive learning. Reaches F1 0.86 on Uni3175.

Multimodal drug-response model coupling cell and molecule foundation models, pretrained on 1.8M perturbation RNA-seq profiles over 22,000 compounds.

Peptide-HLA immunogenicity prediction with a BiLSTM ensemble, inside a pipeline that finds microbial epitopes mimicking tumor neoantigens.

Molecular property prediction foundation model pretrained on computed descriptors. Wins 75% of Polaris tasks and 97% of MoleculeACE assays.

Bulk transcriptome foundation model, 150M parameters over ~20,000 protein-coding genes. Imputes masked expression at Pearson r = 0.954.

Text-guided protein editing framework with disentangled structure and function latents, edited by rewriting either description at inference time.

Structure-based drug design model pairing SE(3)-equivariant diffusion with retrieval of pocket-matched scaffolds to generate ligands for a target.

MSA design model generating alignments from protein language model embeddings to improve folding accuracy on orphan and low-homology proteins.

Graph-level self-supervised pretraining for 3D molecules, reconstructing whole-molecule geometry to sharpen quantum property and force prediction.

Multimodal slide-level pathology foundation model trained by clinical-dialogue supervision on 2.3M whole-slide images and 14M Q&A pairs.

Antibody language model pretrained on 402 million OAS sequences, matching far larger antibody LMs on repertoire tasks at 125M parameters.

De novo peptide sequencing from tandem mass spectra, using curriculum learning and iterative self-refinement to stabilize non-autoregressive decoding.

Protein-protein binding affinity and interface hotspot prediction from sequence alone, using protein language models fine-tuned on SKEMPI 2.0.

Contrastive language-image model for fMRI functional decoding, predicting cognitive tasks, concepts, and domains from brain activation maps.

Multimodal viral foundation model over nucleotide and protein sequence, built for virus discovery, function annotation, and antibody design.

Discrete diffusion model for protein sequence design in an all-atom SELFIES representation, reaching non-canonical and modified amino acid residues.

RNA inverse folding with an attention-free RWKV language model that designs sequences for a target secondary structure with tunable G-C content.

Viral protein language model that predicts a virus's animal host from one protein sequence, generalizing to rare and unseen hosts at 18M parameters.

EEG foundation model with unified spatio-temporal attention and channel-permutation equivariance across unseen electrode montages.

Diffusion model that co-designs binder sequence and backbone for arbitrary protein targets, pretrained on 706,360 protein-protein complexes.

Vision foundation model for MRI, pretrained on 6.9 million slices across 18 body locations for label-efficient segmentation and classification.

Proteome-scale protein dynamics prediction from sequence or structure, predicting residue flexibility, correlations, and conformational states.