Skip to content

Latest commit

 

History

History
41 lines (30 loc) · 2.11 KB

README.md

File metadata and controls

41 lines (30 loc) · 2.11 KB
Raw

Machine Learning Modelling to Predict the Efficacy of Cancer Treatment Drugs

List of Changeable Script Parameters

  • input_dir: Directory path where input files are located, and where output files will be written.
  • models: List of regression models to be evaluated.
  • num_sp: Number of splits for cross-validation.
  • num_rep: Number of repetitions for cross-validation. Other hyperparameters within models and training functions.

Input Files

Option 1: Read in provided csv file

The following raw data file can be read in by setting the input_dir variable to the directory containing the file.

  • raw_data_erbb1_ic50.csv: CSV file containing the data on EGFR protein inhibitors, including canonical smiles and IC50 values.

Option 2: Fetch data from CHEMBL Alternatively, by setting the fetch_chembl variable to TRUE, one can obtain the data directly from the CHEMBL database.

Output Files Generated

The following files will be written directly in the input_dir that was set at the beginning of the script. Files highlighted in orange and intermediate outputs that are used in subsequent steps within the script.

  • erbb1_bothassay_neglog10_ic50.csv: Processed dataset with transformed IC50 values.
  • cb_pb_fingerprints.csv: Molecular fingerprints data.
  • df_pb_cb_for_model_building.csv: Final dataset used for model training.
  • evaluations_with_cv.csv: Evaluation metrics from cross-validation.
  • test_results.csv: Final test results for the optimized model.
  • final_feature_importance.csv: Feature importance from the optimized RandomForest model.

List of Custom Functions

  1. logm: Converts IC50 values from nM to -log(M).
  2. mol_descriptors: Generates molecular descriptors from SMILES.
  3. morgan_fpts: Generates Morgan fingerprints from SMILES.
  4. train_evaluate_model_with_cv: Trains models from list of models and performs cross-validation.
  5. plot_learning_curve: Plots the learning curve for models from list of models.

Supplementary File (Drawing fingerprint bits of interest)

  • Open the DrawFingerprints.ipynb (requires that for_fingerprint_visualization.csv is in same folder)
  • Run notebook file to see bit 343 and 1366 visualizations