Table 3.

Following are some of the examples of common machine learning algorithms which are applied in ADMET prediction

Algorithm	Summary
Random forest (RF)	An ensemble learning method that constructs many decision trees and outputs class or mean prediction
Support vector machine (SVM)	A supervised learning method. The examples are mapped in space and classes separated by a hyperplane
Neural network	A simple neural network has input, hidden and output layers
K nearest neighbors (KNN)	A non-parametric method that uses the K closest training examples in the feature space and classifies objects by a majority vote or in regression uses the average of the values of the nearest members
Naive Bayes (NB)	A probabilistic classifier, considers features to contribute independently to the probability
Deep learning (DNN)	Uses multiple layers of a neural network, where each layer uses the output from the previous one. It can learn multiple levels of representations at different levels of abstraction