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Tetko, I.V.* ; Solov'ev, V.P.* ; Antonov, A.V. ; Yao, X.* ; Doucet, J.P.* ; Fan, B.* ; Hoonakker, F.* ; Fourches, D.* ; Jost, P.* ; Lachiche, N.* ; Varnek, A.*

Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.

J. Chem. Inf. Model. 46, 808-819 (2006)
Verlagsversion DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2006
HGF-Berichtsjahr 0
ISSN (print) / ISBN 0021-9576
e-ISSN 1520-5142
Zeitschrift Journal of Chemical Information and Modeling
Quellenangaben Band: 46, Heft: 2, Seiten: 808-819 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503700-001
DOI 10.1021/ci0504216
PubMed ID 16563012
Erfassungsdatum 2006-12-12
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