Official implementation of pre-training via denoising for TorchMD-NET

An atom-bond transformer-based message passing neural network for molecular property prediction.

🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

Predict optical properties of molecules with machine learning.

[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"

Exploring QSAR Models for Activity-Cliff Prediction

[ICML 2023] Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

An efficient curriculum learning-based strategy for molecular graph learning

The official open-source repository for AutoMolDesigner, an easy-to-use Python application dedicated to automated molecular design.

The code base for AWARE, a graph representation learning method published at TMLR

Code and Data for the paper: Graph Sampling-based Meta-Learning for Molecular Property Prediction [IJCAI2023]

KDD-23 Automated 3D Pre-Training for Molecular Property Prediction

Package for TwinBooster. Enables fast and powerful zero-shot molecular property prediction.

Machine learning for molecular property prediction

Graduation Design

IUPAC-based large-scale molecular pre-trained model for property prediction and molecular generation

3rd place solution for 2022 Samsung AI Challenge (Materials Discovery)

This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric

Collection of Machine Learning and GNN methods for Molecular Property Prediction Task

MUBen: Benchmarking the Uncertainty of Molecular Representation Models