--- a/README.md
+++ b/README.md
@@ -1,41 +1,41 @@
-# 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
+# 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