Renmin University of China

Vision-language model that reads molecular structure images, translating them to SMILES, captions, and properties via chemical-bond topology.

Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.

Generative scientific foundation model that writes proteins, ligands and their binding interfaces as tokens in one shared grammar, at 1B to 8B scale.

DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.

Single-cell foundation model built on masked discrete diffusion, jointly generating gene identities and expression values from 59 million cells.

Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.

DNA foundation model pretrained by supervised genomic profile prediction, using mixture-of-experts routing across species and assay types.

Molecular dynamics emulator generating time-coarsened trajectories for small molecules, peptides, and proteins from one shared atomic representation.

De novo binder design across small molecules, peptides, and antibodies from one geometric latent diffusion model over graphs of molecular blocks.

Ternary complex structure predictor for PROTACs and molecular glues, placing E3 ligase, degrader, and target protein in one SE(3)-equivariant pass.

De novo protein backbone generator built on rectified quaternion flow matching, reaching 0.972 designability with far fewer sampling steps.

Peptide binder design by mimicking the binding interface of a known receptor or antibody, generating all-atom peptides through latent diffusion.

Protein complex interface quality assessment model that pairs persistent homology barcodes with a graph attention network to predict DockQ scores.

All-atom 3D molecular foundation model pretrained across small molecules, proteins, and complexes with an E(3)-equivariant denoising objective.

Encoder-decoder framework unifying molecules, proteins, and natural language with SELFIES notation for cross-modal drug discovery tasks.