Papers

Robust deep learning-based protein sequence design using ProteinMPNN.

Evolutionary-scale prediction of atomic-level protein structure with a language model.

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Genomic benchmarks: a collection of datasets for genomic sequence classification.

Predicting cellular responses to complex perturbations in high-throughput screens.

Transfer learning enables predictions in network biology.

De novo design of protein structure and function with RFdiffusion.

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

Universal Cell Embeddings: A Foundation Model for Cell Biology

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

scGPT: toward building a foundation model for single-cell multi-omics using generative AI.

Large-scale foundation model on single-cell transcriptomics.

Chai-1: Decoding the molecular interactions of life

Protein language models learn evolutionary statistics of interacting sequence motifs.

Sequence modeling and design from molecular to genome scale with Evo.

Boltz-1 Democratizing Biomolecular Interaction Modeling

A cell atlas foundation model for scalable search of similar human cells.

Nucleotide Transformer: building and evaluating robust foundation models for human genomics.

CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.