MoleOOD
Molecular classifier
An implementation of a molecular representation learning method with substructure invariance for out-of-distribution generalization.
Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
60 stars
2 watching
6 forks
Language: Python
last commit: almost 2 years ago
Linked from 1 awesome list
graph-classificationmolecule-representation-learningout-of-distribution-generalizationpytorch
lmmentel/awesome-python-chemistryRelated projects:
| Repository | Description | Stars |
|---|---|---|
 zjulearning/graph_level_drug_discovery | A Python project that uses machine learning to improve the representation of molecules in drug discovery | 60 |
 aspuru-guzik-group/selfies | A Python library that provides a robust representation of semantically constrained graphs, specifically for molecules in chemistry. | 679 |
 atomistic-machine-learning/dtnn | An open-source Python framework for developing machine learning models to predict quantum-mechanical observables of molecular systems. | 77 |
 drorlab/atom3d | Enables machine learning on three-dimensional molecular structure by providing tools and datasets for working with 3D molecular data | 303 |
| atomistic-machine-learning/schnetpack | A toolbox for training and applying deep neural networks to predict atomistic properties of molecules and materials | 789 |
 mwydmuch/napkinxc | A fast and simple library for multi-class and multi-label classification | 64 |
 cxhernandez/molencoder | A PyTorch-based implementation of an autoencoder for molecular data processing and encoding. | 86 |
 qmlcode/qml | A toolkit for representing and learning properties of molecules and solids using quantum machine learning concepts | 199 |
 digital-chemistry-laboratory/morfeus | A Python package for calculating molecular features. | 164 |
 sergioburdisso/pyss3 | A Python package implementing an interpretable machine learning model for text classification with visualization tools | 336 |
 benedekrozemberczki/seal-ci | A PyTorch implementation of a semi-supervised graph classification model that learns hierarchical representations from labeled and unlabeled graph data. | 208 |
 shaharharel/cdn_molecule | Automates molecule design and drug discovery using machine learning models | 13 |
 yuetan031/fedstar | This project implements a federated learning algorithm for non-IID graph classification tasks by leveraging structural knowledge sharing. | 58 |
 wengong-jin/hgraph2graph | A framework for generating molecular graphs using structural motifs and chemical properties. | 378 |
 pharo-ai/naive-bayes-classifier | A simple implementation of a multinomial Naive Bayes classifier for natural language processing and machine learning tasks | 1 |