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

🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

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

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

Predict optical properties of molecules with machine learning.

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

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

Exploring QSAR Models for Activity-Cliff Prediction

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

An efficient curriculum learning-based strategy for molecular graph learning

Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure PyTorch backend.

Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints (ECFPs)

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

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

A GNN model for the prediction of pure component vapor pressures.

Rotationally Equivariant Hypergraph Neural Networks (EquiHGNN)

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

UQ4DD: Uncertainty Quantification for Drug Discovery

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

Machine learning for molecular property prediction