Loading…
Protein language model for variant effect prediction and de novo sequence design, conditioned on Gene Ontology embeddings of molecular function.
Protein sequence-structure co-design model conditioned on Gene Ontology function embeddings, sampling residues and backbone angles together.
De novo protein design model that co-generates sidechains, backbone, and sequence in one flow-matching process instead of backbone only.
Controllable DNA sequence design conditioned on cell type, transcription factor, or activity signal, in GPT- and BERT-style transformer variants.
Multi-LLM consensus framework for automated cell type annotation in scRNA-seq data, outperforming prior methods by ~15% in mean accuracy.
Chemical language model that tokenizes each atom by its functional group, giving transferable embeddings for molecular property prediction.