An atom-bond transformer-based message passing neural network for molecular property prediction.
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Updated
Mar 8, 2023 - Python
An atom-bond transformer-based message passing neural network for molecular property prediction.
Multiscale exploration of informative latent features for accurate deep eutectic solvents viscosity prediction
Official Implementation of Multi-Masked Aggregators for Graph Neural Networks in Pytorch and PyTorch Geometric
Graph Neural Network creation module, implemented in Tensorflow 2 with examples using the module and the iGNNition library for fast GNN prototyping.
This project implements mechanisms to mitigate over-squashing, improving GNN performance on tasks requiring deep Networks.
Melty Molecule is a molecular property prediction system that leverages graph neural networks to predict melting points of chemical compounds. The model uses a hybrid architecture combining Principal Neighborhood Aggregation (PNA) and Graph Isomorphism Networks (GIN) with edge updates and virtual node connectivity.
A message-passing neural network (MPNN) model for predicting molecular sigma-profiles from SMILES strings. The model supports two prediction modes: MSE model and Composite model.
Seven graph neural networks (GCN, MPNN, DCNN, GIN, GAT, GraphSAGE, AttentiveFP) in PyTorch Geometric for molecular property prediction: ESOL solubility from SMILES, with train/predict CLI and tests.
A Message-passing neural network (MPNN) for molecular property prediction
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