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Found 5 papers, 5 papers with code
E(3)-Equivariant Mesh Neural Networks
Triangular meshes are widely used to represent three-dimensional objects.
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with high binding affinities to arbitrary protein targets, guided by a novel multimodal deep neural network built based on graph Transformers as the prior for the generative model.
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs
Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions.
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macromolecules.
Ranked #2 on
Graph Regression
on Peptides-struct
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes.
