scikit-fingerprints/MoleculeNet_PCBA
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.… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_PCBA.
MoleculeNet PCBA
PCBA (PubChem BioAssay) dataset [[1]](#1), part of MoleculeNet [[2]](#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.
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
<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