First Steps

Command Line Interface (CLI)

After installing the chem_mat_data package, you should have access to the cmdata command line interface which allows to interact with the remote file share server to, for example, list all the available datasets or to download them into a local directory.

Help Option

You can use the --help option to get a list of all the available commands:

cmdata --help

Simple Commands

To view a list of all the available datasets on the remote file share server, you can use the list command:

cmdata list

This command will produce a list view as shown below

You can then use the download command to download one of the datasets by using its unique string name:

cmdata download "clintox"

This command will, for example, download a raw clintox.csv file into the current working directory. This raw CSV dataset format consists of a list of SMILES molecule representations and their corresponding target value annotations - in this case a classification label of the molecules toxicity.

Programming Interface (API)

Alternatively, the chem_mat_data functionality can be used programmatically as part of python code. The package provides each dataset either in raw or processed/graph format (For further information on the distincation visit the Loading Datasets) documentation.

Raw Datasets

You can use the load_smiles_dataset function to download the raw dataset format. This function will return the dataset as a pandas.DataFrame object which contains a "smiles" column along with the specific target value annotations as separate data frame columns.

import pandas as pd
from chem_mat_data import load_smiles_dataset

df: pd.DataFrame = load_smiles_dataset('clintox')
print(df.head())

Graph Datasets

You can also use the load_graph_dataset function to download the same dataset in the pre-processed graph representation. This function will return a list of dict objects which contain the full graph representation of the corresponding molecules.

from rich.pretty import pprint
from chem_mat_data import load_graph_dataset

graphs: list[dict] = load_graph_dataset('clintox')
example_graph = graphs[0]
pprint(example_graph)

For more details on the structure of this graph representation, please refer to the Graph Representation documentation.

(Insert an example of how to train a PyG model with this representation)