Deep Learning with Graph Representation of Bio-Molecules to estimate physical Properties
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Updated
Nov 21, 2022 - Jupyter Notebook
Deep Learning with Graph Representation of Bio-Molecules to estimate physical Properties
Predict binding affinity of ligand-protein complexes using Graph Neural Networks. The model is implemented using PyTorch Geometric and based on the method in "Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks"
Machine Learning Research to Advance Simulation Science
[a.a. 23/24] A. Nazzaro, L. Costante
Brain graph super-resolution using graph neural networks.
PyTorch code for an effective way of making a Molecular Graph Dataset in Torch Geometric involving a pair of graphs from chemical SMILE strings
Trying to apply Deep RL + Geometric DL to graphs exploration
Assignments done under course COL761-Data Mining
A project implementing better evaluation scenarios for community models for malicious content detection, and meta-learning GNNs to achieve better downstream adaptation.
This is the code repo for Violin, an IJCAI 2023 paper.
Collection of GNN architectures for 3D shapes and graphs using NN Template
Toxic video game classification with graph neural networks
A customized version of the Relational Aware Graph Attention Network for large scale EHR records.
The goal of this project is to provide a theoretic analysis, explaining the effect of homophily on Graph Neural Network Performace, that leverages on recent results in random family networks and their geodesics, by exploring the structural effects of homophily.
GNN model to learn latent space representations of glycans on an atomic level
Masters Thesis - Reproducible Knowledge Distillation on Graphs.
Protein Secondary Structure Prediction through Hybrid Integration of Transformers and Graph Neural Networks
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