All Competitors

Conditional denoising diffusion model that designs antigen-specific TCR CDR3β sequences conditioned on peptide-MHC targets and germline V-genes.

Generative transformer for phylogenetic inference that transduces sets of unaligned molecular sequences directly into Newick-format trees.

Protein binder generator producing receptor-conditioned binders from sequence alone, using a sparse Mixture-of-Experts transformer with no 3D input.

Reinforcement-learning generative framework for multi-objective RNA codon optimization that generalizes across six species and five RNA types.

Family of ten compact GPT-2 decoder-only DNA language models spanning BPE vocabularies from 16 to 8192 tokens, built for lossless genome compression.

Reinforcement learning framework that fine-tunes the ProGen2-OAS antibody language model with GRPO to cut germline bias in generated sequences.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

Bulk RNA-seq foundation model learning normalization-robust transcriptome representations via TF-IDF gene ordering and masked gene modeling.

Unified all-atom generative model for biomolecular structure prediction, binder filtering, and controllable protein and nanobody design.

Reasoning LLM that predicts antimicrobial susceptibility of clinical bacterial isolates and supplies mechanistic explanations for each prediction.

Fine-tuned Enformer derivative that annotates cis-regulatory elements from DNA sequence, emitting enhancer, promoter, and insulator class labels.

Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.

Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.

Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.

Specificity foundation model predicting small-molecule drug-target binding from sequence, scored as cross-modal retrieval without docking or assays.

Transformer that predicts protein-protein interactions at residue resolution, spanning mutations, PTMs, peptide-MHC binding, and disease variants.

CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.

Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.

Enzyme-substrate specificity model that scores catalytic pairs from sequence with a physics-derived dual-encoder and a contrastive objective.

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

Peptide-MHC binding specificity model that frames presentation as cross-modal retrieval, aligning peptide and MHC encoders by contrastive learning.

Foundation model that predicts microRNA-mRNA target specificity from sequence, using a dual-encoder trained with a symmetric contrastive objective.

Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.

Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.