---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: LRGB Peptides-struct
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: "peptides_struct.csv"
---
# LRGB Peptides-struct

Peptides-struct (Peptides structural) dataset, part of Long Range Graph Benchmark (LRGB) [[1]](#1). It is intended to be used through 
[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.

The task is to predict structural properties of peptides. Note that this is raw data, whereas the original paper [[1]](#1) specifies
that targets should be standardized (mean 0, standard deviation 1) before training and evaluation. scikit-fingerprints does this by
default in the loader function, otherwise this should be performed manually.

| **Characteristic** |  **Description**  |
|:------------------:|:-----------------:|
|        Tasks       |         11        |
|      Task type     |     regression    |
|    Total samples   |       15535       |
|  Recommended split | stratified random |
| Recommended metric |        MAE        |

## References
<a id="1">[1]</a> 
Dwivedi, Vijay Prakash, et al.
"Long Range Graph Benchmark"
Advances in Neural Information Processing Systems 35 (2022): 22326-22340
https://proceedings.neurips.cc/paper_files/paper/2022/hash/8c3c666820ea055a77726d66fc7d447f-Abstract-Datasets_and_Benchmarks.html