Massively Multitask Networks for Drug Discovery
Paper • 1502.02072 • Published
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MoleculeNet PCBA
PCBA (PubChem BioAssay) dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through 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).
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
[1] Ramsundar, Bharath, et al. "Massively multitask networks for drug discovery" arXiv:1502.02072 (2015) https://arxiv.org/abs/1502.02072
[2] 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
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