Collection of Machine Learning and GNN methods for Molecular Property Prediction Task
-
Updated
May 24, 2024 - Jupyter Notebook
Collection of Machine Learning and GNN methods for Molecular Property Prediction Task
Pretraining Techniques for Graph Transformers
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
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints
Code for The Catalyst Deep Neural Networks (Cat-DNNs) in Singlet Fission Property Prediction
⚗️ Samsung AI Challenge for Scientific Discovery 5위 솔루션입니다.
Graduation Design
Samsung AI Challenge for Scientific Discovery, Samsung Advanced Institute of Technology and Dacon, ~2021.09.27
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction. IJCAI 2024
Molecular-property prediction with sparsity
Machine learning 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.
3rd place solution for 2022 Samsung AI Challenge (Materials Discovery)
KDD-23 Automated 3D Pre-Training for Molecular Property Prediction
IUPAC-based large-scale molecular pre-trained model for property prediction and molecular generation
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
The code base for AWARE, a graph representation learning method published at TMLR
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]
Add a description, image, and links to the molecular-property-prediction topic page so that developers can more easily learn about it.
To associate your repository with the molecular-property-prediction topic, visit your repo's landing page and select "manage topics."