---
license: unknown
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: MoleculeNet PCBA
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: "pcba.csv"
---

# MoleculeNet PCBA

PCBA (PubChem BioAssay) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through 
[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.

The task is to predict biological activity against 128 bioassays, generated by high-throughput screening (HTS). All tasks are binary active/non-active.

Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros.

| **Characteristic** |      **Description**     |
|:------------------:|:------------------------:|
|        Tasks       |            128           |
|      Task type     | multitask classification |
|    Total samples   |          437929          |
|  Recommended split |         scaffold         |
| Recommended metric |       AUPRC, AUROC       |

**Warning:** in newer RDKit vesions, 2 molecules from the original dataset are not read correctly due to disallowed
hypervalent states of some atoms (see [release notes](https://github.com/rdkit/rdkit/releases/tag/Release_2024_09_1)).
This version of the PCBA dataset contains manual fixes for those molecules, removing additional hydrogens, e.g. `[AlH3] -> [Al]`.
In OGB scaffold split, used for benchmarking, both molecules are in the training set. Applied mapping is:
```
"CC(=O)O[AlH3]OC(C)=O" -> "CC(=O)O[Al]OC(C)=O"
"CCCCO[AlH3](OCCCC)OCCCC" -> "CCCCO[Al](OCCCC)OCCCC"
```

## References
<a id="1">[1]</a> 
Ramsundar, Bharath, et al.
"Massively multitask networks for drug discovery"
arXiv:1502.02072 (2015)
https://arxiv.org/abs/1502.02072

<a id="2">[2]</a> 
Wu, Zhenqin, et al.
"MoleculeNet: a benchmark for molecular machine learning."
Chemical Science 9.2 (2018): 513-530
https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a