5 papers
All-Atom GPCR-Ligand Simulation via Residual Isometric Latent Flow
Jiying Zhang, Shuhao Zhang, Pierre Vandergheynst +1
G-protein-coupled receptors (GPCRs), primary targets for over one-third of approved therapeutics, rely on intricate conformational transitions to transduce signals. While Molecular…
MEIDNet: Multimodal generative AI framework for inverse materials design
Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst +1
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastiv…
Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
Aditya Sengar, Jiying Zhang, Pierre Vandergheynst +1
Simulating the long-timescale dynamics of biomolecules is a central challenge in computational science. While enhanced sampling methods can accelerate these simulations, they rely…
Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
Ali Hariri, Álvaro Arroyo, Alessio Gravina +6
ChebNet, one of the earliest spectral GNNs, has largely been overshadowed by Message Passing Neural Networks (MPNNs), which gained popularity for their simplicity and effectiveness…
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Álvaro Arroyo, Alessio Gravina, Benjamin Gutteridge +5
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely succe…