title: Extremely Randomized Machine Learning Methods for Compound Activity Prediction.
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.
authors.affiliation: Faculty of Mathematics and Computer Science, Jagiellonian University, Lojasiewicza 6, 30-348 Krakow, Poland. wojciech.czarnecki@uj.edu.pl.
authors.forename: Wojciech M
authors.initials: WM
authors.lastname: Czarnecki
authors.affiliation: Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland. smusz@if-pan.krakow.pl.|Faculty of Chemistry, Jagiellonian University, Ingardena 3, 30-060 Krakow, Poland. smusz@if-pan.krakow.pl.
authors.forename: Sabina
authors.initials: S
authors.lastname: Podlewska
authors.affiliation: Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland. bojarski@if-pan.krakow.pl.
authors.forename: Andrzej J
authors.initials: AJ
authors.lastname: Bojarski
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D055808:Drug Discovery / Q000379:methods*; D000069550:Machine Learning; D008962:Models, Theoretical
keywords: compounds classification; extreme entropy machine; extremely randomized trees; virtual screening