Trustworthiness, the Key to Grid-Based Map-Driven Predictive Model Enhancement and Applicability Domain Control

In chemography, grid-based maps sample
molecular descriptor space
by injecting a set of nodes, and then linking them to some regular
2D grid representing the map. They include self-organizing maps (SOMs)
and generative topographic maps (GTMs). Grid-based maps are predictive
because any compound thereupon projected can “inherit”
the properties of its residence node(s)node properties themselves
“inherited” from node-neighboring training set compounds.
This Article proposes a formalism to define the trustworthiness of
these nodes as “providers” of structure–activity
information captured from training compounds. An empirical four-parameter
node trustworthiness (NT) function of density (sparsely populated
nodes are less trustworthy) and coherence (nodes with training set
residents of divergent properties are less trustworthy) is proposed.
Based upon it, a trustworthiness score T is used
to delimit the applicability domain (AD) by means of a trustworthiness
threshold TT. For each parameter setup, success of ensuing inside-AD
predictions is monitored. It is seen that setup-specific success levels
(averaged over large pools of prediction challenges) are highly covariant,
irrespectively of the targets of prediction challenges, of the (classification
or regression) type of problems, of the specific parametrization,
and even of the nature (GTM or SOM) of underlying maps. Thus, success
levels determined on the basis of regression problems (445 target-specific
affinity QSAR sets) on GTMs and levels returned by completely unrelated
classification problems (319 target-specific active-/inactive-labeled
sets) on SOMs were seen to correlate to a degree of 70%. Therefore,
a common, general-purpose setup of the herein proposed parametric
AD definition was shown to generally apply to grid-based map-driven
property prediction problems.