List of protein (enzymes and PPIs) conformations and molecular dynamics using generative artificial intelligence and deep learning

Assignments done under course COL761-Data Mining

Full Stack Graph Machine Learning: Theory, Practice, Tools and Techniques

DiTEC research

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.

MatTen: Equivariant Graph Neural Nets for Tensorial Properties of Materials

An implementation of the Autoregressive Diffusion Model for Graph Generation from [Kong et al. 2023]

List of molecular design using Generative AI and Deep Learning

Accompanying code for the AISTATS 2024 paper BLIS-Net: Classifying and Analyzing Signals on Graphs

Python Framework for An Investigation into Unsupervised GNN Learning Environments

Solving the Job-Shop Scheduling Problem (JSSP) with Graph Neural Networks (GNNs).

A project implementing better evaluation scenarios for community models for malicious content detection, and meta-learning GNNs to achieve better downstream adaptation.

Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation (WWW-2024)

Interface-aware molecular generative framework for protein-protein interaction modulators

[a.a. 23/24] A. Nazzaro, L. Costante

A contrastive approach for 3d protein prediction.

A Survey of Learning from Graphs with Heterophily

Official code for the SDM2022 paper -- SSSNET: Semi-Supervised Signed Network Clustering.

This repository contains the code for the work on protein-ligand interaction with GNNs and XAI