############################ Training Classifier ############################ HOME_LOC="x" SCRIPT_LOC=$HOME_LOC/Classifier_Weight/training_classifiers DATA_LOC=$HOME_LOC/Classifier_Weight/training_data_cleaned OBJECTIVE='hemolysis' # nf/solubility/hemolysis WT='smiles' # wt/smiles DATA_FILE="hemo_${WT}_with_embeddings" LOG_LOC=$SCRIPT_LOC/src/logs DATE=$(date +%m_%d) MODEL_TYPE='svm_gpu' # xgb/enet_gpu/svm_gpu SPECIAL_PREFIX="${MODEL_TYPE}-${OBJECTIVE}-${WT}_new" cd $SCRIPT_LOC python -u train_ml.py \ --dataset_path "${DATA_LOC}/${OBJECTIVE}/${DATA_FILE}" \ --out_dir "${SCRIPT_LOC}/${OBJECTIVE}/${MODEL_TYPE}_${WT}_new" \ --model "${MODEL_TYPE}" \ --n_trials 20 > "${LOG_LOC}/${DATE}_${SPECIAL_PREFIX}.log" 2>&1 echo "Script completed at $(date)" ############################ Training Classifier (NN) ############################ HOME_LOC="x" SCRIPT_LOC=$HOME_LOC/Classifier_Weight/training_classifiers DATA_LOC=$HOME_LOC/Classifier_Weight/training_data_cleaned OBJECTIVE='hemolysis' # nf/solubility/hemolysis WT='smiles' #wt/smiles DATA_FILE="nf_${WT}_with_embeddings_unpooled" LOG_LOC=$SCRIPT_LOC/src/logs DATE=$(date +%m_%d) MODEL_TYPE='cnn' #mlp/cnn/transformer SPECIAL_PREFIX="${MODEL_TYPE}-${OBJECTIVE}-${WT}" # Create log directory if it doesn't exist mkdir -p $LOG_LOC cd $SCRIPT_LOC python -u train_nn.py \ --dataset_path "${DATA_LOC}/${OBJECTIVE}/${DATA_FILE}" \ --out_dir "${SCRIPT_LOC}/${OBJECTIVE}/${MODEL_TYPE}_${WT}_20" \ --model "${MODEL_TYPE}" \ --n_trials 20 > "${LOG_LOC}/${DATE}_${SPECIAL_PREFIX}_20.log" 2>&1 echo "Script completed at $(date)" ############################ Training Regressor ############################ HOME_LOC="x" SCRIPT_LOC=$HOME_LOC/Classifier_Weight/training_classifiers DATA_LOC=$HOME_LOC/Classifier_Weight/training_data_cleaned OBJECTIVE='permeability_pampa' # permeability_pampa/permeability_caco2 WT='smiles' # wt/smiles DATA_FILE="pampa_${WT}_with_embeddings" LOG_LOC=$SCRIPT_LOC/src/logs DATE=$(date +%m_%d) MODEL_TYPE='svr' # xgb_reg/enet_gpu/svr SPECIAL_PREFIX="${MODEL_TYPE}-${OBJECTIVE}-${WT}" # Create log directory if it doesn't exist mkdir -p $LOG_LOC cd $SCRIPT_LOC python -u train_ml_regression.py \ --dataset_path "${DATA_LOC}/${OBJECTIVE}/${DATA_FILE}" \ --out_dir "${SCRIPT_LOC}/${OBJECTIVE}/${MODEL_TYPE}_${WT}10" \ --model "${MODEL_TYPE}" \ --n_trials 10 > "${LOG_LOC}/${DATE}_${SPECIAL_PREFIX}10.log" 2>&1 echo "Script completed at $(date)" ############################ Training Regressor (NN) ############################ HOME_LOC="x" SCRIPT_LOC=$HOME_LOC/Classifier_Weight/training_classifiers DATA_LOC=$HOME_LOC/Classifier_Weight/training_data_cleaned OBJECTIVE='permeability_caco2' # permeability_pampa/permeability_caco2 WT='smiles' # wt/smiles DATA_FILE="caco2_${WT}_with_embeddings_unpooled" LOG_LOC=$SCRIPT_LOC/src/logs DATE=$(date +%m_%d) MODEL_TYPE='mlp' #mlp/cnn/transformer SPECIAL_PREFIX="${MODEL_TYPE}-${OBJECTIVE}-${WT}" # Create log directory if it doesn't exist mkdir -p $LOG_LOC cd $SCRIPT_LOC python -u train_nn_regression.py \ --dataset_path "${DATA_LOC}/${OBJECTIVE}/${DATA_FILE}" \ --out_dir "${SCRIPT_LOC}/${OBJECTIVE}/${MODEL_TYPE}_${WT}" \ --model "${MODEL_TYPE}" \ --n_trials 200 > "${LOG_LOC}/${DATE}_${SPECIAL_PREFIX}.log" 2>&1 echo "Script completed at $(date)" ############################ Training Binding Affinity Predictor ############################ HOME_LOC="x" SCRIPT_LOC=$HOME_LOC/Classifier_Weight/training_classifiers DATA_LOC=$HOME_LOC/Classifier_Weight/training_data_cleaned OBJECTIVE='binding_affinity' WT='smiles' #wt/smiles STATUS='pooled' #pooled/unpooled DATA_FILE="pair_wt_${WT}_${STATUS}" LOG_LOC=$SCRIPT_LOC/src/logs DATE=$(date +%m_%d) SPECIAL_PREFIX="${OBJECTIVE}-${WT}-${STATUS}" # Create log directory if it doesn't exist mkdir -p $LOG_LOC cd $SCRIPT_LOC python -u binding_training.py \ --dataset_path "${DATA_LOC}/${OBJECTIVE}/${DATA_FILE}" \ --mode "${STATUS}" \ --out_dir "${SCRIPT_LOC}/${OBJECTIVE}/wt_${WT}_${STATUS}" \ --n_trials 200 > "${LOG_LOC}/${DATE}_${SPECIAL_PREFIX}.log" 2>&1 echo "Script completed at $(date)"