---
license: cc-by-4.0
tags:
- chemistry
size_categories:
- 10K<n<100K
---

# Dataset Card for CREMP

Conformer-rotamer ensembles of macrocyclic peptides.

## Dataset Details

### Dataset Description

CREMP: A resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical tight-binding DFT calculations.

- **Curated by:** Colin Grambow, Hayley Weir, Kangway Chuang
- **Funded by:** Genentech
- **License:** Creative Commons Attribution 4.0 International

### Dataset Sources

- **Repository:** [Zenodo](https://doi.org/10.5281/zenodo.7931444)
- **Paper:** [CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning](https://doi.org/10.1038/s41597-024-03698-y)
- **Code:** [github.com/Genentech/cremp](https://github.com/Genentech/cremp)

## Dataset Structure

Each row contains the macrocyclic peptide sequence (which is cyclic, so "C.R.E.M.P" and "R.E.M.P.C" correspond to the same peptide), its SMILES, as well as metadata from the CREST simulation used to generate the conformer ensemble. The conformers are stored in the "sdf" column, which contains SDF format strings with all of the conformers for a given sequence.

The `train`, `val`, and `test` splits were created by splitting randomly based on sequence in 95/5/5 ratios.

## Citation

```
@article{grambow2024cremp,
    title = {{CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning}},
    author = {Grambow, Colin A. and Weir, Hayley and Cunningham, Christian N. and Biancalani, Tommaso and Chuang, Kangway V.},
    year = {2024},
    journal = {Scientific Data},
    doi = {10.1038/s41597-024-03698-y},
    pages = {859},
    number = {1},
    volume = {11}
}
```