| --- |
| 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 |
