Models (15)
Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.
Lightweight 7B vision-language foundation model from Microsoft Research, released research-only under the Microsoft Research License, that generates radiology findings from chest X-rays.
A biomedical foundation model for joint segmentation, detection, and recognition across nine imaging modalities using natural language prompts.
A transformer protein language model using integrative co-evolutionary pre-training to capture both short-range and long-range residue interactions from sequence alone.
Lightweight variant of AlphaFlow achieving ~47x faster conformational ensemble sampling by fine-tuning only AlphaFold's structure module with frozen Evoformer.
Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.
Whole-slide pathology foundation model pretrained on 1.3 billion tiles from 171,189 clinical WSIs. Achieves state-of-the-art on 25 of 26 pathology benchmark tasks.
Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.
Microsoft Research multimodal LLM that generates the findings section of a chest X-ray report from a single frontal image using a CXR-specific vision encoder and Vicuna-7B.
Sequence-first protein generation framework using discrete diffusion over evolutionary alignments, enabling controllable de novo design without structure.
Graph neural network framework for antigen-specific antibody CDR design, combining a pre-trained antibody language model with one-shot sequence and structure generation.
A biomedical vision-language assistant from Microsoft Research, adapted from LLaVA via curriculum learning on PubMed Central figure-caption pairs and GPT-4-generated instructions.
Multimodal biomedical foundation model trained on 15M PubMed Central figure-caption pairs via contrastive learning, achieving state-of-the-art zero-shot performance across imaging modalities.
A GPT-2-based generative transformer pretrained on 15M PubMed abstracts for biomedical text generation and mining, including relation extraction and question answering.
CNN-based protein language model series showing convolutions match transformer performance on sequence pretraining while scaling linearly with sequence length.