Working on it ⚒️
- Chai-1: Decoding the molecular interactions of life
- Simulating 500 million years of evolution with a language model
- OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
- Multistate and functional protein design using RoseTTAFold sequence space diffusion
- Protein interactions in human pathogens revealed through deep learning
- De novo design of pH-responsive self-assembling helical protein filaments
- Substrate interactions guide cyclase engineering and lasso peptide diversification
- Opportunities and challenges in design and optimization of protein function
- Self-driving laboratories to autonomously navigate the protein fitness landscape
- Diffusion models in bioinformatics and computational biology
- Score-based generative modeling for de novo protein design
- Machine learning-enabled retrobiosynthesis of molecules
- DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
- Protein structure generation via folding diffusion
- Language models of protein sequences at the scale of evolution enable accurate structure prediction
- Computational design of soluble and functional membrane protein analogues
- Accurate structure prediction of biomolecular interactions with AlphaFold 3
- Generalized biomolecular modeling and design with RoseTTAFold All-Atom
- Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA
- Predicting multiple conformations via sequence clustering and AlphaFold2
- Clustering predicted structures at the scale of the known protein universe
- Computational design of transmembrane pores
- De novo protein design by deep network hallucination
- Role of backbone strain in de novo design of complex α/β protein structures
- Design of protein-binding proteins from the target structure alone
- Accurate computational design of three-dimensional protein crystals
- Improving de novo protein binder design with deep learning
- Uncovering new families and folds in the natural protein universe
- Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
- Towards a structurally resolved human protein interaction network
- A structural biology community assessment of AlphaFold2 applications
- Improved AlphaFold modeling with implicit experimental information
- ColabFold: making protein folding accessible to all
- Accurate prediction of protein structures and interactions using a three-track neural network
- Highly accurate protein structure prediction for the human proteome
- Highly accurate protein structure prediction with AlphaFold
- Improved protein structure prediction using potentials from deep learning
- CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2
- AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
- Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15
- Fast and effective protein model refinement using deep graph neural networks
- De novo design of potent and selective mimics of IL-2 and IL-15
- Receptor subtype discrimination using extensive shape complementary designed interfaces
- De novo design of bioactive protein switches
- Accurate de novo design of hyperstable constrained peptides
- The coming of age of de novo protein design
- Computational design of self-assembling cyclic protein homo-oligomers
- De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy
- Accurate design of co-assembling multi-component protein nanomaterials
- De novo design of a non-local β-sheet protein with high stability and accuracy
- De novo metalloprotein design
- Constructing ion channels from water-soluble α-helical barrels
- Language models enable zero-shot prediction of the effects of mutations on protein function
- MSA Transformer
- Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences
- Challenges in protein-folding simulations