Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.
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.
Tetko
Igor V
IV
Institute of Bioorganic & Petrochemistry, Kiev, Ukraine.
Solov'ev
Vitaly P
VP
Antonov
Alexey V
AV
Yao
Xiaojun
X
Doucet
Jean Pierre
JP
Fan
Botao
B
Hoonakker
Frank
F
Fourches
Denis
D
Jost
Piere
P
Lachiche
Nicolas
N
Varnek
Alexandre
A
D000465:Algorithms; D005063:Europium / Q000737:chemistry; D007476:Ionophores / Q000737:chemistry*; D016014:Linear Models; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D009942:Organometallic Compounds / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012834:Silver / Q000737:chemistry; D012986:Software Validation
D007476:Ionophores; D009942:Organometallic Compounds; D012834:Silver; D005063:Europium