{
  "fetched_at": "2026-10-19T11:22:56Z",
  "source_url": "https://www.kaggle.com/competitions/bgudrugchallenge2022/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Three files are included\n\n*   **X_y_train.csv** - the training set\n*   **X_test.csv** - the test set\n*   **y_rest_submission_example.csv** - a sample submission file in the correct format\n\n",
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    {
      "name": "Description",
      "content": "```\n```\nPredict the TP53 label for every molecule based on various features that were extracted from the  SMILES (“Simplified Molecular Input Line Entry System\") of the molecule using DeepChem (https://deepchem.io/)\n\n\n```\n```",
      "isPublished": false,
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    },
    {
      "name": "Evaluation",
      "content": "\nThe evaluation metric for this competition is  Accuracy (https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification). \n\nThe Competition leaderboard has two parts.\n\nThe public leaderboard provides publicly visible submission scores based on a representative sample of the test data. This leaderboard is visible throughout the competition.\n\nThe private leaderboard, by contrast, tracks model performance using the remainder of the test data. The private leaderboard thus has final say on whose models are best, and hence, who the winners and losers of the Competition will be. Which subset of data is calculated on the private leaderboard or a submission’s performance on the private leaderboard is not released to users until the competition has been closed.\n\n\n## Submission Format\n\n**For every molecule in the  test dataset**, submission files should contain two columns: `ID` and `y_pred` which contains your label prediction of this row. See file y_test_submission_example that was created using random forest.\n",
      "isPublished": false,
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    },
    {
      "name": "rules",
      "content": "Please see the doc published on Moodle (task 2)\n",
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      "hasIsPublished": false,
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}