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# Your brief name for this protocol
Name: Tscore/Maestro/GOLD
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# All software packages used and their versions, one per line
Software: Maestro_Schrodinger Suite_2015-2/CCDC_Gold Suite_2016/Scikit-learn_0.17

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# List any number of key computational parameters, one per line
Parameters: Use PH values listed in Data_set_fxr_crystallization_conditions.csv
Parameters: autoscale = 0.2
Parameters: GBDT(from Scikit-learn):params = {'n_estimators': 20000, 'max_depth': 8, 'min_samples_split': 6, 'learning_rate': 0.005, 'loss': 'ls', 'subsample': 0.7, 'max_features': 'sqrt', 'min_samples_leaf': 3, 'random_state': None}
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# Provide free text description of your method below. Use as many lines as you need. 
Method: Use four sets of data as the training set. First set was chosen from PDBbind database, protein in each complex was similar to the FXR protein provided in the packet. Second set was chosen from RCSB Protein Data Bank, protein in each complex has similar binding site to the FXR protein. Third set was also chosen from RCSB Protein Data Bank, smile similarity search was perfomed to all ligands (FXR_1 - FXR_102) and as a result complexes with similar ligands were chosen. Fourth set was generated by combining PDBbind refined sets (v2007, v2013 and v2015). Gold suite was used to generate decoy sets for each complex. Structure and atom type based in house machine learning protocol was used to generate features for both training set and testing set. Gradient boosting tree regression was used to get predict values. 
