metadata
dataset_info:
config_name: raw_data
features:
- name: SMILES
dtype: string
- name: split
dtype: string
- name: dataset
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 127171194
num_examples: 1930035
- name: val
num_bytes: 221548
num_examples: 3387
- name: test
num_bytes: 226468
num_examples: 3540
download_size: 45145194
dataset_size: 127619210
configs:
- config_name: raw_data
data_files:
- split: train
path: raw_data/train-*
- split: val
path: raw_data/val-*
- split: test
path: raw_data/test-*
Dataset Details
Dataset Description
Molecular Sets (MOSES) is a benchmark platform for distribution learning based molecule generation. Within this benchmark, MOSES provides a cleaned dataset of molecules that are ideal of optimization. It is processed from the ZINC Clean Leads dataset.
- Curated by:
- License: CC BY 4.0
Dataset Sources
- Article about original dataset
- Link to publication of associated dataset - zinc
- Github repository concerning the dataset
Citation
BibTeX:
@article{10.3389/fphar.2020.565644,
title={{M}olecular {S}ets ({MOSES}): {A} {B}enchmarking {P}latform for {M}olecular {G}eneration {M}odels},
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},
journal={Frontiers in Pharmacology},
year={2020}
}