DE-HNN: An effective neural model for Circuit Netlist representation
The run-time for optimization tools used in chip design has grown with the complexity of
designs to the point where it can take several days to go through one design cycle which …
designs to the point where it can take several days to go through one design cycle which …
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
Graph Transformers (GTs) such as SAN and GPS are graph processing models that
combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to …
combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to …
MeGraph: capturing long-range interactions by alternating local and hierarchical aggregation on multi-scaled graph hierarchy
Graph neural networks, which typically exchange information between local neighbors, often
struggle to capture long-range interactions (LRIs) within the graph. Building a graph …
struggle to capture long-range interactions (LRIs) within the graph. Building a graph …
Multimodal pretraining for unsupervised protein representation learning
VT Duy Nguyen, T Son Hy - Biology Methods and Protocols, 2024 - academic.oup.com
Proteins are complex biomolecules essential for numerous biological processes, making
them crucial targets for advancements in molecular biology, medical research, and drug …
them crucial targets for advancements in molecular biology, medical research, and drug …
E (3)-Equivariant Mesh Neural Networks
Triangular meshes are widely used to represent three-dimensional objects. As a result,
many recent works have addressed the need for geometric deep learning on 3D meshes …
many recent works have addressed the need for geometric deep learning on 3D meshes …
Multimodal protein representation learning and target-aware variational auto-encoders for protein-binding ligand generation
Without knowledge of specific pockets, generating ligands based on the global structure of a
protein target plays a crucial role in drug discovery as it helps reduce the search space for …
protein target plays a crucial role in drug discovery as it helps reduce the search space for …
Protein design by directed evolution guided by large language models
Directed evolution, a strategy for protein engineering, optimizes protein properties (ie,
fitness) by a rigorous and resource-intensive process of screening or selecting among a vast …
fitness) by a rigorous and resource-intensive process of screening or selecting among a vast …
Target-aware variational auto-encoders for ligand generation with multimodal protein representation learning
NK Ngo, TS Hy - bioRxiv, 2023 - biorxiv.org
Without knowledge of specific pockets, generating ligands based on the global structure of a
protein target plays a crucial role in drug discovery as it helps reduce the search space for …
protein target plays a crucial role in drug discovery as it helps reduce the search space for …
Spatio-Spectral Graph Neural Networks
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning
on graph-structured data. However, key limitations of l-step MPGNNs are that their" receptive …
on graph-structured data. However, key limitations of l-step MPGNNs are that their" receptive …
Symmetry-preserving graph attention network to solve routing problems at multiple resolutions
Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have
achieved reasonable improvement in accuracy and computation time with the adaptation of …
achieved reasonable improvement in accuracy and computation time with the adaptation of …