All Competitors

Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.

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

Transformer predicting gene expression from histone modifications, using promoter-enhancer Hi-C interactions to capture distal regulatory effects.

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

DNA language model for interpretable prediction of 4mC, 5hmC, and 6mA methylation sites across species, using multi-scale k-mer BERT encoders.

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

Protein structure prediction model pairing SE(3)-equivariant networks with a coarse-grained representation to fold sequences fast, without MSA inputs.

Human-in-the-loop cell segmentation framework enabling custom model training from as few as 100-200 corrected annotations.

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

Word2vec-based language model trained on 360 million microbial genes that predicts gene function from genomic context without sequence homology.

Medical vision-language pretraining framework that injects structured medical knowledge into radiology image-text learning for VQA and retrieval.

Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.

Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.

Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.

Pretrained transformer for cell type annotation of scRNA-seq data. Trained on 1.1M cells; outperforms supervised methods on cross-dataset transfer.

Deep learning model predicting DNA methylation regulatory variants at CpG sites in the human brain, fine-mapping psychiatric disorder risk loci.

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

Motif-oriented DNA pre-training framework that adds motif prediction to an ELECTRA generator-discriminator setup for motif-aware genomic embeddings.

Autoregressive protein language model based on GPT-2 that generates de novo protein sequences sampling unexplored regions of protein space.

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

Transformer model for de novo peptide sequencing that reads amino acid sequences directly from tandem mass spectra, with no protein sequence database.

RNA methylation site predictor combining multiple sequence encodings with dilated convolution and BiLSTM layers to identify m6A and m1A sites.

Protein language model family built on CNNs rather than transformers, matching transformer quality while scaling linearly with sequence length.

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