All Competitors

Molecular foundation model that turns SMILES into 2048-dimensional embeddings from multiple 3D conformations for ADMET and virtual screening.

Multimodal foundation model that embeds histology, transcriptomics, and clinical records in one space for patient stratification and target discovery.

Generative chemistry foundation model pairing a text-and-SMILES language backbone with a 3D molecular point-cloud encoder for prediction and design.

Open model that jointly predicts biomolecular structure and small-molecule binding affinity, approaching FEP+ accuracy in seconds on a single GPU.

Molecular embedding model that turns SMILES into SE(3)-invariant vectors for property prediction, similarity search, and compound clustering.

Histopathology foundation model pretrained on over 1 million H&E slides from 800,000 patients. Leads the HEST spatial gene expression benchmark.

Genomic foundation model trained on 9.3 trillion DNA base pairs across all domains of life, with 40B parameters and a 1-million-token context.

Histopathology vision transformer with 1.1B parameters, pretrained on patches from 500,000 H&E whole-slide images across 4,000 clinical practices.

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

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