Classification of Metal Binders by Naıve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modelingстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 12 февраля 2020 г.
Аннотация:Here, we report two-class classification models for
organic molecules (“ligands”) able to bind various metal
cations in water. The modeling was performed on 30 data
sets, each corresponding to a particular metal, using the
Naıve Bayes method and the ISIDA fragment descriptors.
The ligands were classified on weak and strong binders
according to threshold of the logarithm of the stability
constant of the 1:1 (metal:ligand) complexes. The “con-
sensus models” consisted each of 50 best individual models
demonstrated a good predictive performance in 5-fold cross
validation: the balanced accuracy (BA) varies from 0.965
(Yb3+ ) to 0.767 (Mg2+ ). The best predictions (BA>0.90) were
obtained for the binders of rare-earth metals (Yb3+, Tm3+,
Er3+, Lu3+, Ho3+, Gd3+ and Dy3+) and Pb2+. For 17 small
external test sets of new ligands, BA varies from 0.800 to 1.
The impact of variables selection on the predictive perform-
ance of the models is discussed.