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Drug Discov Today. 2015 Mar;20(3):318-31. doi: 10.1016/j.drudis.2014.10.012. Epub 2014 Nov 4.

Machine-learning approaches in drug discovery: methods and applications.

Author information

1
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.

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.

PMID:
25448759
DOI:
10.1016/j.drudis.2014.10.012
[Indexed for MEDLINE]

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