Official implementation of pre-training via denoising for TorchMD-NET
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Updated
Mar 2, 2023 - Python
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.
[ICML 2023] Hierarchical Grammar-Induced Geometry for Data-Efficient 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.
An efficient curriculum learning-based strategy for molecular graph learning
Code and Data for the paper: Graph Sampling-based Meta-Learning for Molecular Property Prediction [IJCAI2023]
Exploring QSAR Models for Activity-Cliff Prediction
The code base for AWARE, a graph representation learning method published at TMLR
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
IUPAC-based large-scale molecular pre-trained model for property prediction and molecular generation
3rd place solution for 2022 Samsung AI Challenge (Materials Discovery)
KDD-23 Automated 3D Pre-Training for Molecular Property Prediction
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
Package for TwinBooster. Enables fast and powerful zero-shot molecular property prediction.
Graduation Design
Samsung AI Challenge for Scientific Discovery, Samsung Advanced Institute of Technology and Dacon, ~2021.09.27
Machine learning for molecular property prediction
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