Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties

Identification
of chemical compounds having desirable properties
is a central goal of screening campaigns. Iterative screening is a
means of surveying a set of compounds, during which their property
values are determined and used as feedback for regression models.
Quantitative models that assess the relationships between chemical
structures and property/activity are repeatedly updated through this
type of cycle, and the efficient sampling of compounds for the subsequent
test is a key factor in the early identification of target compounds.
Nevertheless, methodological approaches to comparisons and to establishing
the degree of extrapolation of sampled compounds, including the effects
of applicability domains, are still required. In the present study,
we conducted a series of virtual experiments to assess the characteristics
of different iterative screening methods. Genetic algorithm-based
partial least-squares regression, support vector regression, Bayesian
optimization with Gaussian Process (GP), and batch-based Bayesian
optimization with GP (GP_batch) were all compared, based on the analysis
of one million compounds extracted from the ZINC database. Our results
show that, irrespective of the diversity of the initial set of compounds,
it was possible to identify a compound having the desired property
value using the appropriate screening method. However, overall, the
GP_batch method was found to be preferable when evaluating properties
either which are difficult to predict or for which a key factor
is present in the set of molecular descriptors.