Sample datasets

ChemPlot provides some sample datasets that can be used to get started right away with exploring the libraries features. These datasets can be loaded with the following function:

from chemplot import load_data

df = load_data("BBBP")

In these case we are loading the BBBP dataset, used in the previous sections of this manual. load_data() returns a pandas DataFrame built using the sample dataset provided as a parameter. Chemplot contains the following sample datasets:

ID	Name	Type	Size
C_1478_CLINTOX_2	Clintox (Toxicity) [1] [2] [3] [4]	Classification	1478
C_1513_BACE_2	BACE (Inhibitor) [5]	Classification	1513
C_2039_BBBP_2	BBBP (Blood-brain barrier penetration) [6]	Classification	2039
C_41127_HIV_3	HIV [7]	Classification	41127
R_642_SAMPL	SAMPL (Hydration free energy) [8]	Regression	642
R_1513_BACE	BACE (Binding affinity) [5]	Regression	1513
R_4200_LOGP	LOGP (Lipophilicity) [9]	Regression	4200
R_1291_LOGS	LOGS (Aqueous Solubility) [10]	Regression	1291
R_9982_AQSOLDB	AQSOLDB (Aqueous Solubility) [11]	Regression	9982

The datasets ID are constructed in the following way:

Name Formatting: type_size_name_num_of_classes.csv

• type: R->Numerical and C->Categorical

• size: Number of instances in the dataset

• name: Name of dataset

• num_of_classes: Number of classes (Categorical only)

You can retrieve the datasets by passing their ID to load_data().

Note

The first 8 datasets in the table are edited versions of the MoleculeNet repository [12].

You can print the available sample datasets to console with ChemPlot using the following function:

from chemplot import info_data

df = info_data()

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References:

[1]

Gayvert, Kaitlyn M., Neel S. Madhukar, and Olivier Elemento. (2016) A data-driven approach to predicting successes and failures of clinical trials. Cell chemical biology 23.10 1294-1301.

[2]

Artemov, Artem V., et al. (2016) Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes. bioRxiv 095653.

[3]

Novick, Paul A., et al. (2013) SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery. PloS one 8.11 e79568.

[4]

Aggregate Analysis of ClincalTrials.gov (AACT) Database.

[5] (1,2)

Subramanian, Govindan, et al. (2016) Computational modeling of β-secretase 1 (BACE-1) inhibitors using ligand based approaches. Journal of chemical information and modeling 56.10 1936-1949.

[6]

Martins, Ines Filipa, et al. (2014) A Bayesian approach to in silico blood-brain barrier penetration modeling. Journal of chemical information and modeling 52.6 (2012): 1686-1697.

[7]

AIDS Antiviral Screen Data.

[8]

Mobley, David L., and J. Peter Guthrie. FreeSolv: a database of experimental and calculated hydration free energies, with input files. Journal of computer-aided molecular design 28.7 711-720.

[9]

Hersey, A. (2015) ChEMBL Deposited Data Set - AZ dataset

[10]

Huuskonen, J. (2000) Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. Journal of Chemical Information and Computer Sciences, 40(3), 773-777.

[11]

Sorkun, M. C., Khetan, A., & Er, S. (2019) AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds. Scientific data, 6(1), 1-8.

[12]

Wu, Zhenqin, et al. (2018) MoleculeNet: a benchmark for molecular machine learning. Chemical science 9.2 513-530.