Labs & Groups (1)
Models (6)
Open-source framework for building RNA and DNA foundation models, featuring WCED pretraining for transcriptomics and SNP-aware encoding for genomics.
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.
A geometric relational graph neural network that learns protein structure representations via geometry-aware message passing and self-supervised pretraining.
Reprograms a frozen English BERT model for antibody CDR sequence infilling via learnable cross-domain projection matrices, without training a new protein language model.
Large-scale chemical language model trained on 1.1 billion SMILES strings using linear attention transformers for molecular property prediction.