There are 1 repository under molecular-property-prediction topic.
Official implementation of pre-training via denoising for TorchMD-NET
๐ฅSamsung AI Challenge 2021 1๋ฑ ์๋ฃจ์ ์ ๋๋ค๐ฅ
[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"
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.
The code base for AWARE, a graph representation learning method published at TMLR
IUPAC-based large-scale molecular pre-trained model for property prediction and molecular generation
3rd place solution for 2022 Samsung AI Challenge (Materials Discovery)
MUBen: Benchmarking the Uncertainty of Pre-Trained Models 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
KDD-23 Automated 3D Pre-Training for Molecular Property Prediction
Samsung AI Challenge for Scientific Discovery, Samsung Advanced Institute of Technology and Dacon, ~2021.09.27
Package for TwinBooster. Enables fast and powerful zero-shot molecular property prediction.
Molecular-property prediction with sparsity
Graduation Design
This repository contains codes and data related to the paper "FunQG: Molecular Representation Learning Via Quotient Graphs". A pre-print version of this paper is currently available at
Pretraining Techniques for Graph Transformers
Collection of Machine Learning and GNN methods for Molecular Property Prediction Task
โ๏ธ Samsung AI Challenge for Scientific Discovery 5์ ์๋ฃจ์ ์ ๋๋ค.