title: Machine-learning approaches in drug discovery: methods and applications.
abstract: During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.
authors.affiliation: Department of Pharmacy, Drug Discovery Laboratory, University of Napoli 'Federico II', via D. Montesano 49, I-80131 Napoli, Italy. Electronic address: antonio.lavecchia@unina.it.
authors.forename: Antonio
authors.initials: A
authors.lastname: Lavecchia
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003663:Decision Trees; D055808:Drug Discovery; D016571:Neural Networks, Computer