All Competitors

Generative RNA foundation model trained on 114 million full-length sequences for de novo design of tRNAs, aptamers, CRISPR guide RNAs, and mRNAs.

Transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, with no 3D structure step.

All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.

Multimodal reverse-translation language model that generates species-aware mRNA coding sequences from protein sequences, conditioned on host taxonomy.

Single-nucleotide-resolution RNA foundation model pretrained on non-coding RNAs with ELECTRA-style replaced-token detection for regulatory inference.

Geometric deep learning model that learns atomic-scale representations of molecular interfaces across proteins, small molecules, and nucleic acids.

Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.

Coding-sequence foundation model for mRNA design, pretrained as a BART denoising encoder-decoder on mRNA from nine taxonomic groups.

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

Graph-attention model that predicts A-to-I RNA editing from sequence and secondary structure, treating RNA as a graph with base-pairing edges.

mRNA language foundation model trained on ~115M protein-coding sequences across the tree of life, unifying mRNA perception and generation.

Multimodal architecture coupling pretrained DNA, RNA, and protein language models with directional cross-attention into one Virtual Cell Embedding.

GPT-style generative language model for mRNA coding sequences, pretrained across bacteria, eukaryotes, and archaea for de novo CDS design.

Structure-aware transformer that makes zero-shot, per-adenosine predictions of ADAR-mediated A-to-I RNA editing to guide therapeutic guide-RNA design.

In-context learning model for cell-free RNA, meta-trained on synthetic tasks from a cfRNA structural causal model for few-shot cancer classification.

RNA inverse-folding model that generates sequences predicted to fold into a target 3D backbone, capturing non-canonical pairs and tertiary motifs.

Single-cell foundation model adapting LLaMA-3.1-8B with LoRA, recasting transcriptomes and protein interaction networks as natural-language Q&A pairs.

RNA interaction foundation model for conditional, zero-shot design of RNA sequences that bind protein, DNA, or RNA targets without retraining.

Conditional codon language model with 150M parameters that generates species-optimized coding sequences from a protein and its taxonomic lineage.

Genomics foundation model that unifies sequence-to-function prediction, DNA language modeling, and generative regulatory design in one backbone.

Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.

Efficient sequence-to-function transformer for regulatory genomics, matching Borzoi-class models while training in about a day on a single GPU.

RNA foundation model pretrained jointly on sequences and secondary structures for structure prediction, homology and splice site classification.

Bimodal masked language model that jointly encodes bulk RNA-seq expression and DNA methylation into patient-level embeddings for cancer genomics.