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Found 17 papers, 13 papers with code
Dirichlet Flow Matching with Applications to DNA Sequence Design
Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models.
AlphaFold Meets Flow Matching for Generating Protein Ensembles
When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling.
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
The runtime of our approach can be amortized at several levels of abstraction, and is particularly favorable for virtual screening settings with a common binding pocket.
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
A significant amount of protein function requires binding small molecules, including enzymatic catalysis.
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
The recent developments of foundation models in computer vision, especially the Segment Anything Model (SAM), allow scalable and domain-agnostic image segmentation to serve as a general-purpose segmentation tool.
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
Ranked #1 on
Blind Docking
on PDBbind
Torsional Diffusion for Molecular Conformer Generation
Molecular conformer generation is a fundamental task in computational chemistry.
Subspace Diffusion Generative Models
Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process.
Ranked #19 on
Image Generation
on CIFAR-10
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning.
ATOM3D: Tasks On Molecules in Three Dimensions
We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure.
Learning from Protein Structure with Geometric Vector Perceptrons
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain.
Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks
A desireable property of accelerometric gait-based identification systems is robustness to new device orientations presented by users during testing but unseen during the training phase.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery.
SGVAE: Sequential Graph Variational Autoencoder
Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity.
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication
Multi-agent reinforcement learning has shown promise on a variety of cooperative tasks as a consequence of recent developments in differentiable inter-agent communication.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
