361 citations · 931 across the 14 of their papers we have counts for
6 papers · 1 filter
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Gabriele Corso, Arthur Deng, Benjamin Fry +3
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Exi…
Dirichlet Flow Matching with Applications to DNA Sequence Design
Hannes Stark, Bowen Jing, Chenyu Wang +4
Discrete diffusion or flow models could enable faster and more controllable sequence generation than autoregressive models. We show that naïve linear flow matching on the simplex i…
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov +6
Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulat…
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Bowen Jing, Ezra Erives, Peter Pao-Huang +3
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensem…
Modeling Molecular Structures with Intrinsic Diffusion Models
Gabriele Corso
Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by…
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing +2
Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat…