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Predicting Protein โ Ligand Interaction by using Deep Learning Models Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Directory Structure ``` scripts/ read_pdb_file.py read_testing_pdb_file.py train_preprocessing_binary.py test_preprocessing_binary.py pos_neg_split.py cnn_binary.py prediction_analysis.py data/ training_data/ testing_data/ train_pro/ train_lig/ test_pro/ test_lig/ 16_8/ fig ``` *** Copy all scripts to ./scripts/ and place training_data and testing_data in ./data/ Creating Training Dataset -------------------- 1. Run read_pdb_file.py on ../data/training_data to obtain all protein and ligand csv files in ./data/train_pro/ python read_pdb_file.py 2. Separate protein and ligand csv files into separate directories at ./data/train_pro and ./data/train_lig mv ./data/train_pro/*lig* ./data/train_lig 3. Make new directory ../data/train_x mkdir ../data/train_x 4. Run train_preprocessing_binary.py on the above csv files. Output will be saved to ../data/train_x python train_preprocessing_binary.py 5. Make new directory ../data/positive_x mkdir ../data/positive_x 6. Run pos_neg_split.py to separate positive samples into ../data/positive_x python pos_neg_split.py 7. Rename ../data/train_x as ../data/negative_x mv ../data/train_x ../data/negative_x 8. Make new directory ../data/16_8/ and shift folders ../data/negative_x and ../data/positive_x inside mkdir ../data/16_8/ mv ../data/negative_x ../data/16_8/ mv ../data/positive_x ../data/16_8/ Creating Test Dataset -------------------- 1. Run read_testing_pdb_file.py on ../data/testing_data to obtain all protein and ligand csv files in ./data/test_pro/ python read_testing_pdb_file.py 2. Separate protein and ligand csv files into separate directories at ./data/test_pro and ./data/test_lig mv ./data/test_pro/*lig* ./data/test_lig 3. Make new directory ../data/test_x mkdir ../data/train_x 4. Run test_preprocessing_binary.py on the above csv files. Output will be saved to ../data/test_x python train_preprocessing_binary.py 5. Shift folders ../data/test_x inside ../data/16_8/ mv ../data/test_x ../data/16_8/ Training Model and Predicting -------------------- 1. Run cnn_binary.py , training information and prediction output will be saved in ../fig/ python cnn_binary.py 2. Run prediction_analysis.py to analyse the training information and prediction output
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