title: How wrong can we get? A review of machine learning approaches and error bars.
abstract: A large number of different machine learning methods can potentially be used for ligand-based virtual screening. In our contribution, we focus on three specific nonlinear methods, namely support vector regression, Gaussian process models, and decision trees. For each of these methods, we provide a short and intuitive introduction. In particular, we will also discuss how confidence estimates (error bars) can be obtained from these methods. We continue with important aspects for model building and evaluation, such as methodologies for model selection, evaluation, performance criteria, and how the quality of error bar estimates can be verified. Besides an introduction to the respective methods, we will also point to available implementations, and discuss important issues for the practical application.
authors.affiliation: Technische Universität Berlin, Department of Computer Science, Franklinstrasse 28/29, D-10587 Berlin, Germany.
authors.forename: Anton
authors.initials: A
authors.lastname: Schwaighofer
authors.forename: Timon
authors.initials: T
authors.lastname: Schroeter
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mika
authors.forename: Gilles
authors.initials: G
authors.lastname: Blanchard
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003663:Decision Trees; D055808:Drug Discovery; D008024:Ligands; D008956:Models, Chemical; D015233:Models, Statistical; D016011:Normal Distribution; D021281:Quantitative Structure-Activity Relationship
chemical_list: D008024:Ligands