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# Your brief name for this protocol
Name: In_house_machine_learning_score/Maestro/GOLD
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# All software packages used and their versions:
Software: Maestro_Schrodinger Suite_2016-2/CCDC_Gold Suite_2016/R

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# List any number of key computational parameters in the following lines:
Parameters: Use PH values listed in Data_set_fxr_crystallization_conditions.csv, use other default parameters in Ligprep of Schrodinger
Parameters: Gold suite with autoscale = 0.2 and goldscore as a scoring function
Parameters: RandomForest with number of trees = 550, using a total of number of features

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# Provide free text description of your method below. Use as many lines as you need. 
# The level of detail should be about what you would write for a publication.  
Method: Method: The training set is from PDBbind refined sets v2015. Gold docking software is used to get decoy sets, and the top poses are selected by our in-house software. All features of the training and test sets are generated by our in house machine learning tool. RandomForst is used to get predicted values.

