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Found 11 papers, 4 papers with code
Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions
Generative models that satisfy hard constraints are crucial in many scientific and engineering applications where physical laws or system requirements must be strictly respected.
Geometric Point Attention Transformer for 3D Shape Reassembly
Shape assembly, which aims to reassemble separate parts into a complete object, has gained significant interest in recent years.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
To address these challenges, we introduce PepHAR, a hot-spot-driven autoregressive generative model for designing peptides targeting specific proteins.
Training Free Guided Flow Matching with Optimal Control
Moreover, existing methods predominately focus on Euclidean data manifold, and there is a compelling need for guided flow methods on complex geometries such as SO(3), which prevails in high-stake scientific applications like protein design.
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry.
Full-Atom Peptide Design based on Multi-modal Flow Matching
Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery.
Categorical Flow Matching on Statistical Manifolds
We introduce Statistical Flow Matching (SFM), a novel and mathematically rigorous flow-matching framework on the manifold of parameterized probability measures inspired by the results from information geometry.
Equivariant Neural Operator Learning with Graphon Convolution
We propose a general architecture that combines the coefficient learning scheme with a residual operator layer for learning mappings between continuous functions in the 3D Euclidean space.
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme.
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
In this work, we propose the Orientation-Aware Graph Neural Networks (OAGNNs) to better sense the geometric characteristics in protein structure (e. g. inner-residue torsion angles, inter-residue orientations).
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature
We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work.
