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AI-based Scoring Functions
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AI-Based Scoring Functions Construction
Example files
Protein file
Actives file
Decoys file
Test file
Result data
Result view
Job name
Protein file
(.pdb)
Actives file
(.mol2/.sdf)
Decoys file
(.mol2/.sdf)
Test file
(.mol2/.sdf)
ML algorithms
Hyperparameter
(optional)
Optimization times
(optional)
Choose protein file...
Choose active file...
Choose decoys file...
Choose test file...
Support Vector Machine
eXtreme Gradient Boosting
Random Forest
RF-2-[n_estimators,max_features]
RF-3-[n_estimators,max_features,max_depth]
RF-4-[n_estimators,max_features,max_depth,criterion]
SVM-1-[kernel]
SVM-2-[C,kernel]
SVM-3-[C,kernel,gamma]
XGB-5-[learning_rate,gamma,max_depth,min_child_weight,subsample]
XGB-7-[XGB-5,colsample_bytree,scale_pos_weight]
XGB-10-[XGB-7,seed,reg_lambda,n_estimators]
Random Forest Hyperparameter
Warning
:improper parameter settings can cause errors !
Name
Default
Range
Setting
Min
Max
Opt
n_estimators
100
int
max_depth
None
int
max_features
auto
['sqrt','log2']
['sqrt','log2']
['sqrt']
['log2']
criterion
gini
['gini','entropy']
['gini','entropy']
['gini']
['entropy']
Support Vector Machine Hyperparameter
Warning
:improper parameter settings can cause errors !
Name
Default
Range
Setting
Min
Max
Opt
C
1.0
float
gamma
scale
float
kernel
rbf
string
['rbf','linear']
['rbf']
['linear']
['poly']
['rbf','poly']
['linear','poly']
['rbf','linear','poly']
eXtreme Gradient Boosting Hyperparameter
Warning
:improper parameter settings can cause errors !
Name
Default
Range
Setting
Min
Max
Opt
learning_rate
0.3
[0,1]
gamma
0
[0,∞)
max_depth
6
[0,∞)
min_child_weight
1
[0,∞)
subsample
1
(0,1]
colsample_bytree
1
(0,1]
scale_pos_weight
1
(0,∞)
seed
0
[0,∞)
reg_lambda
0
[0,∞)
n_estimators
100
[1,∞)