All Competitors

RNA language model with 650M parameters pretrained on 36 million non-coding RNA sequences, generalizing structure prediction to unseen RNA families.

Protein large language model adapted from LLaMA-2 that unifies sequence generation and superfamily classification in one 7B-parameter framework.

Generative language model for single-cell transcriptomics with 368M parameters, unifying cell type annotation, batch integration, and cell generation.

RNA language model trained on multiple sequence alignments of Rfam families, predicting secondary structure and solvent accessibility from homology.

Generative language model trained on 558 million antibody sequences for infilling-based design of CDR loops and full-length immunoglobulin sequences.

Protein language models from 151M to 6.4B parameters, trained on over a billion sequences for sequence generation and zero-shot fitness prediction.

Encoder-decoder framework unifying molecules, proteins, and natural language with SELFIES notation for cross-modal drug discovery tasks.

Open large language models for natural science, fine-tuned on physics, chemistry, and materials science literature with automated instruction tuning.

Bilingual protein language model that translates bidirectionally between amino acid sequences and the 3Di structural alphabet for inverse folding.

Unsupervised transformer language model for TCR-epitope binding prediction that generalizes to unseen epitopes without needing negative examples.

Multi-species genomic foundation model swapping k-mer tokenization for byte pair encoding, matching Nucleotide Transformer with 21x fewer parameters.

Biomedical vision-language assistant for question answering on radiology and pathology images, adapted from LLaVA on PubMed Central captions.

Single-cell foundation model pre-trained on 22 million transcriptomes, using rank-based gene encoding for clustering and trajectory inference.

Masked DNA language model trained on over 800 vertebrate genomes and conditioned on species identity to learn conserved regulatory sequence features.

Parameter-efficient protein language model that matches larger models such as ESM-2 on protein prediction tasks using under 10% of the parameters.

RNA language model pre-trained on 2M+ pre-mRNA sequences from 72 vertebrate species for splice-site prediction and variant effect analysis.

Antibody CDR design model that reprograms a frozen English BERT for sequence infilling, avoiding training a dedicated protein language model.

Scientific large language model trained on 48 million papers, textbooks, and reference works to store, combine, and reason about scientific knowledge.

Generative transformer pretrained on PubMed abstracts for biomedical text generation and mining, including relation extraction and question answering.

Genome-scale language model trained on prokaryotic genes and SARS-CoV-2 genomes to model viral evolution and flag emerging variants of concern.

Large-scale chemical language model trained on 1.1 billion SMILES strings using linear attention transformers for molecular property prediction.

RNA foundation model pretrained on 23.7 million non-coding RNA sequences, producing embeddings for structure prediction, annotation, and RNA design.

Meta AI's family of protein language models scaled to 15B parameters, paired with ESMFold for fast, alignment-free atomic-level structure prediction.

BERT-based antibody language model pretrained on 57M B cell receptor sequences for paratope prediction and convergent antibody discovery.