metadata
license: cc-by-4.0
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/rp13-3k3h
- Year: 2022
- Authors: Polykovskiy, Daniil, Zhebrak, Alexander, Sanchez-Lengeling, Benjamin, Golovanov, Sergey, Tatanov, Oktai, Belyaev, Stanislav, Kurbanov, Rauf, Artamonov, Aleksey, Aladinskiy, Vladimir, Veselov, Mark, Kadurin, Artur, Johansson, Simon, Chen, Hongming, Nikolenko, Sergey, Aspuru-Guzik, Alan, Zhavoronkov, Alex
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| inchi | input | International Chemical Identifier (InChI) for the | |
| smiles | input | Simplified molecular-input line-entry system (SMIL |
Splits
- data: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/rp13-3k3h")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("foundry_moses_v1.1")
Citation
@misc{https://doi.org/10.18126/rp13-3k3h
doi = {10.18126/rp13-3k3h}
url = {https://doi.org/10.18126/rp13-3k3h}
author = {Polykovskiy, Daniil and Zhebrak, Alexander and Sanchez-Lengeling, Benjamin and Golovanov, Sergey and Tatanov, Oktai and Belyaev, Stanislav and Kurbanov, Rauf and Artamonov, Aleksey and Aladinskiy, Vladimir and Veselov, Mark and Kadurin, Artur and Johansson, Simon and Chen, Hongming and Nikolenko, Sergey and Aspuru-Guzik, Alan and Zhavoronkov, Alex}
title = {Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models}
keywords = {machine learning, foundry, molecules, materials, moses}
publisher = {Materials Data Facility}
year = {root=2022}}
License
CC-BY 4.0
This dataset was exported from Foundry-ML, a platform for materials science datasets.