All Competitors

A signed heterogeneous graph foundation model pretrained on the SIGMA-KG knowledge graph for zero-shot drug mode-of-action, clinical response, and drug-drug interaction prediction.

A motif-aware graph diffusion transformer for controllable molecular generation that transfers to unseen properties by learning only lightweight task embeddings with the generator frozen.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Unified science foundation model from Microsoft Research treating molecules, proteins, RNA, DNA, and materials as a shared sequence language for cross-domain generation.

Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.

Multi-view molecular foundation model that integrates graph, image, and text representations via late fusion for molecular property and target prediction.

An enhanced T5-based encoder-decoder that unifies molecule, protein, and text understanding via IUPAC integration and multi-task instruction tuning.

Pre-training framework bridging molecules, proteins, and natural language using T5 with SELFIES representations for cross-modal biological understanding.

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