All Competitors

Conditional discrete diffusion model for protein variant generation, with a calibrated identity dial controlling drift from a wild-type sequence.

Unified bio-language Mixture-of-Experts model spanning DNA, protein sequence and structure, and biological text across eight task families.

Latent diffusion model for PTM-aware protein sequence design, using ControlNet-style conditioning to steer generation toward chosen PTM sites.

Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.

Protein sequence embedding model, contrastively fine-tuned from ESM-2, that places functionally and structurally related proteins close together.

All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.

Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.

Protein language model aligning ESM sequence embeddings with molecular dynamics trajectories for zero-shot mutation effect and stability prediction.

Reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal LLM understanding expert with a Boltz-1 diffusion expert.

Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.

Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.

Protein-protein docking model adapting AlphaFold-Multimer with a docking module and flow-matching training to assemble subunits without MSAs.

Leakage-controlled multimodal model predicting within-species relative protein expression across 385 bacterial species, with transfer to unseen phyla.

Protein function prediction model that fuses sequence, structure, text, and interaction embeddings with learned gating to assign Gene Ontology terms.

Chemical language models pretrained on SMILES for therapeutic peptides, natively representing non-canonical residues, cyclization, and conjugation.

Contrastive dual-encoder model for DIA proteomics, embedding peptides and spectra in a shared space for zero-shot peptide-spectrum matching.

Encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble descriptors.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

Autoregressive language model trained on 37 million intrinsically disordered region sequences, generating IDRs given flanking folded domains.

464M-parameter structure prediction and design model that improves antibody-antigen complex accuracy over Protenix-v1 and adds generative VHH design.

Multimodal diffusion model that co-designs protein sequence and 3D structure around cofactors and small molecules for de novo heme enzyme design.

Graph attention model that learns context-aware protein embeddings from protein-protein interaction, co-expression, and tissue association networks.

Protein foundation model for de novo enzyme design that co-designs sequence and 3D structure under small-molecule ligand guidance, at 730M parameters.

Compact 167M-parameter protein language model built on a multiplicative LSTM, giving zero-shot variant effect and fitness prediction from sequence.