Predictive cartography of metal binders using generative topographic mappingстатья
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: 3 декабря 2017 г.
Авторы:Baskin I.I.,
Solov’ev V.P.,
Bagatur’yants A.A.,
Varnek A.
Аннотация:Generative topographic mapping (GTM)
approach is used to visualize the chemical space of organic
molecules (L) with respect to binding a wide range of 41
different metal cations (M) and also to build predictive
models for stability constants (logK) of 1:1 (M:L) com-
plexes using “density maps,” “activity landscapes,” and
“selectivity landscapes” techniques. A two-dimensional
map describing the entire set of 2962 metal binders reveals
the selectivity and promiscuity zones with respect to indi-
vidual metals or groups of metals with similar chemical
properties (lanthanides, transition metals, etc). The GTM-
based global (for entire set) and local (for selected subsets)
models demonstrate a good predictive performance in the
cross-validation procedure. It is also shown that the data
likelihood could be used as a definition of the applicability
domain of GTM-based models. Thus, the GTM approach
represents an efficient tool for the predictive cartography
of metal binders, which can both visualize their chemi-
cal space and predict the affinity profile of metals for new
ligands.