foundry-ml/foundry_moses_v1-1

---
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](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/rp13-3k3h](https://doi.org/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)

```python
from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/rp13-3k3h")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("foundry_moses_v1.1")
```

## Citation

```bibtex
@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](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*