All Competitors

Protein language model interpretability adapter that factors ESM2 and ProtBERT embeddings into named biochemical features plus a residual subspace.

Multimodal graph foundation model fusing single-cell expression, biomedical text, and signaling networks, pretrained on ~80M sc/snRNA-seq profiles.

General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.

Controllable protein sequence generation that conditions latent diffusion on physicochemical properties aligned to natural-language prompts.

Cell-free RNA language model for liquid biopsy, fusing RNA sequence embeddings with cfRNA abundance across 306 billion pretraining tokens.

Open therapeutics foundation models from Google, built on Gemma-2, for drug-discovery property prediction and conversational reasoning.

Text-to-protein generative model designing de novo sequences from plain-language function descriptions, with designs confirmed by crystallography.

Medical vision-language model trained with reinforcement learning for generalizable reasoning across eight imaging modalities and five question types.

Synthesis planning model that generates full synthetic routes from Enamine building blocks and reaction templates for any target small molecule.

Vision-language model for open-vocabulary mouse behavior analysis, describing multi-view video and pose kinematics in natural language.

Single-cell foundation model that fuses scRNA-seq profiles with text, pairing a cell encoder with an LLM for cell annotation and clustering.

Protein language model that tokenizes sequence, backbone structure, and text into one vocabulary for function prediction, design, and fold editing.

Multimodal LLM unifying 12-lead ECG time series, ECG images, and text for grounded, clinician-aligned electrocardiogram interpretation.

Goal-oriented de novo molecule design with an instruction-tuned LLM that honors property targets, substructure constraints, and numeric values.

2B-parameter medical vision-language model that uses reinforcement learning to show interpretable reasoning for radiology visual question answering.

Non-coding RNA language model using masked discrete diffusion to unify sequence generation with representation learning, trained on 30M ncRNAs.

Chest X-ray vision-language model that drafts the findings section of a radiology report, at 7B parameters small enough to run on a single GPU.

Instruction-tuned LLMs for multi-property molecule optimization, rewriting a hit compound to improve three or more drug properties at once.

Histopathology foundation model for whole-slide cancer diagnosis, with a ViT-L encoder pretrained by DINOv2 on a million-slide hospital archive.

Unified science foundation model treating molecules, proteins, RNA, DNA, and materials as one sequence language, in 1B, 8B, and 46.7B sizes.

Pathology vision-language model that adds lightweight adaptors and multi-granular prompt learning for few-shot whole-slide image classification.

Protein question-answering model that fuses sequence and structure into an LLM prompt as virtual tokens, answering free-form questions about function.

Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.