All Competitors

Transformer-based generative language model for de novo RNA design, pretrained on 16 million non-coding RNA sequences from RNAcentral.

Diffusion model for protein-protein docking that unifies pose sampling and energy-based ranking, works without MSAs, and generalizes to new targets.

De novo peptide binder design framework that targets specific motifs, including disordered regions and conserved epitopes, from target sequence alone.

Lightweight AlphaFlow variant that fine-tunes only AlphaFold's structure module, keeping the Evoformer frozen to cut conformational sampling cost.

Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.

Multimodal generative protein language model reasoning jointly over protein sequence, structure, and function, trained at 98B parameters.

Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

AI-designed CRISPR-Cas9 gene editor generated by protein language models trained on 1.2 million CRISPR operons and shown to edit the human genome.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

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

Generative image perturbation autoencoder predicting cellular morphological responses to chemical and genetic perturbations from control images.

Diffusion model for synthesizing single-cell RNA-seq data, with guided generation of specific cell types, rare cells, and developmental trajectories.

Diffusion model for programmable protein design that jointly samples structures and sequences, conditioned on symmetry, shape, or text prompts.

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

Discrete diffusion model for protein sequence and MSA generation, enabling controllable de novo design directly in sequence space without structure.

Radiology foundation model that reads interleaved 2D and 3D scans with text for diagnosis, visual question answering, and report generation.

Google's generalist multimodal biomedical AI that encodes clinical text, medical images, and genomics with a single set of weights across 14 tasks.

GPT-style DNA foundation model trained on over 200 billion base pairs of mammalian genomes for sequence generation, classification, and regression.

Generative transformer that translates single-cell transcriptomes into proteomes, inferring missing protein abundance from RNA expression alone.

Generative medical visual question answering model that pairs a vision encoder with a language model, trained on the 227k-pair PMC-VQA dataset.

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

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

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