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Molecules 2015, 20(11), 20107-20117; doi:10.3390/molecules201119679

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

1
Faculty of Mathematics and Computer Science, Jagiellonian University, Lojasiewicza 6, 30-348 Krakow, Poland
2
Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland
3
Faculty of Chemistry, Jagiellonian University, Ingardena 3, 30-060 Krakow, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Willett
Received: 14 August 2015 / Revised: 14 August 2015 / Accepted: 27 October 2015 / Published: 9 November 2015
(This article belongs to the Special Issue Chemoinformatics)
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Abstract

Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure. View Full-Text
Keywords: virtual screening; compounds classification; extreme entropy machine; extremely randomized trees virtual screening; compounds classification; extreme entropy machine; extremely randomized trees
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Czarnecki, W.M.; Podlewska, S.; Bojarski, A.J. Extremely Randomized Machine Learning Methods for Compound Activity Prediction. Molecules 2015, 20, 20107-20117.

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