Performance of Machine Learning Methods for Ligand-Based Virtual Screening

Title: Performance of Machine Learning Methods for Ligand-Based Virtual Screening

Volume: 12 Issue: 4

Author(s): Dariusz Plewczynski, Stephane A.H. Spieser and Uwe Koch

Affiliation:

Keywords: QSAR, machine learning, virtual screening, drug discovery

Abstract: Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

Cite this article as:

Plewczynski Dariusz, Spieser A.H. Stephane and Koch Uwe, Performance of Machine Learning Methods for Ligand-Based Virtual Screening, Combinatorial Chemistry & High Throughput Screening 2009; 12 (4) . https://dx.doi.org/10.2174/138620709788167962