The datasets to be used in this Challenge have been generated and curated by researchers at the
Brookhaven National Laboratory (BNL) in collaboration with other DOE (Department of Energy)
National Laboratories who have joined forces to combat the COVID-19 pandemics. The provided
datasets contain the docking scores of molecules (i.e., drug candidates) on SARS-CoV-2 protein
targets. Drug candidates are represented in SMILES strings and selected from known drug
databases such as ENAMINE [1], ZINC [2] and DrugBank [3]. All SMILES strings have been
canonicalized. The COVID-19 protein targets were provided by researchers at Argonne National
Laboratory (ANL). The Docking scores were obtained from Autodock 4.2 [4] and then collected
and organized into a CSV file, where rows represent molecules and columns represent different
docking targets.
The whole dataset includes docking scores of 300,457 molecules on 18 different COVID-19
related protein docking targets. Part of this data will be provided for training and initial validation.
The rest will be held out for evaluating the performance of the models trained by the participants.
The training/validation dataset will include the SMILES string representing 270,000 molecules
and their docking scores against different targets. In the test set, only the SMILES strings will be
provided for 30,457 molecules without their docking scores. The participants will need to train
their own model that can be used for accurate prediction of the docking scores of these
molecules on different targets. The dataset can be obtained from the github repo [5] created for
the Challenge: https://github.com/BC3D/BC3D_2021

# References & Resources

1. https://enamine.net/compound-libraries/diversity-libraries

2. https://zinc.docking.org/

3. https://go.drugbank.com/releases/latest

4. The original docking score data used for creation of the training/testing/validation datasets have been obtained from AutoDock. Further information regarding AutoDock can be found at the following URL: http://autodock.scripps.edu/

5. https://github.com/BC3D/BC3D_2021