Table 1 Some important parameters used in QSAR modeling

kNN

The number of predictors at each node = 1–10

RF

The number of predictors at each node = 105, the number of trees = 230

SVM (RBF)

The kernel width σ = 0.03125, the penalty parameter C = 2, and ε in the loss function = 0.05

RVM (Laplace)

The kernel width σ = 0.044

laGP

The initial values of lengthscale = 5, the initial values of nugget = 0.1

MPLE

The number of individual perceptrons = 18, the number of units in the hidden layer = 5–8

XGBoost

Step size shrinkage = 0.1, maximum depth of a tree = 7, the max number of iterations = 69