Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization

The growing computational abilities
of various tools that are applied in the broadly understood field
of computer-aided drug design have led to the extreme popularity of
virtual screening in the search for new biologically active compounds.
Most often, the source of such molecules consists of commercially
available compound databases, but they can also be searched for within
the libraries of structures generated in silico from existing ligands.
Various computational combinatorial approaches are based solely on
the chemical structure of compounds, using different types of substitutions
for new molecules formation. In this study, the starting point for
combinatorial library generation was the fingerprint referring to
the optimal substructural composition in terms of the activity toward
a considered target, which was obtained using a machine learning-based
optimization procedure. The systematic enumeration of all possible
connections between preferred substructures resulted in the formation
of target-focused libraries of new potential ligands. The compounds
were initially assessed by machine learning methods using a hashed
fingerprint to represent molecules; the distribution of their physicochemical
properties was also investigated, as well as their synthetic accessibility.
The examination of various fingerprints and machine learning algorithms
indicated that the Klekota−Roth fingerprint and support vector
machine were an optimal combination for such experiments. This study
was performed for 8 protein targets, and the obtained compound sets
and their characterization are publically available at http://skandal.if-pan.krakow.pl/comb_lib/.