Table 1.

Overview of popular regression and learning algorithms used for virtual screening

	Classification	Regression	Variable selection	Explanatory	Virtual screening
SOM	Yes, useful for visualizing global data trends	No	Various techniques show success extracting pertinent dependent variables by grouping compounds in the same target class together	Yes, when the pertinent dependent variables are optimized	Identified purinergic receptor antagonists from a virtual combinatorial library [25]
Binary QSAR	Yes	No	No	No	Showed superior enrichment rates when compared to Bayesian Classifiers and PLS [26]
Bayesian Classifier	Yes	Yes	Descriptors are weighted based on how well each divides the training data	Yes if the significance of each descriptor can be extracted	Performed poorly compared to SVM, kNN, ANN and Decision trees [27]
Decision trees	Yes	No	Descriptors that best divide one class from another are used to separate the data	Variables used in the tree(s) suggest activity dependency	Slightly outperformed a Bayes Classifier in a comparison study [27]
PLS variants	Yes	Yes	Variable selection techniques are commonly added above PLS model building	Yes, when a variable selection technique is incorporated	Ligands for various GPCR targets were successfully enriched from a test database [19]
ANN	Yes	Yes	Performed internally	No	Comparable enrichment rates in a direct comparison to SVM and kNN [27]
SVM	Yes	Yes	Performed internally	Yes, if the weights of each descriptor are explicitly solved.	Identified previously characterized Dopamine D1 Inhibitors and suggested new hits [29]
kNN	Yes	Yes	Commonly a genetic algorithm or simulated annealing is used	Descriptors selected by multiple models imply relevance to the target property	Identified several anticonvulsant compounds that were experimentally confirmed [28]