Maximum Common Substructure Searching in Combinatorial Make-on-Demand Compound Spaces

Commercial make-on-demand
compound spaces have become increasingly
popular within the past few years. Since these libraries are too large
for enumeration, they are usually accessed using combinatorial fragment
space technologies like FTrees-FS and SpaceLight. Although both search
types are of high practical impact, they lack the ability to search
for precise structural features on the atomic level. To address this
important use case, we developed SpaceMACS enabling efficient and
precise maximum common induced substructure (MCIS) similarity and
substructure searches within chemical fragment spaces. SpaceMACS enumerates
a user-defined number of compounds in a multistep procedure. First,
substructures of the query are extracted and matched to all fragments
of the space. Then partial results are combined to actual compounds
of the space. In this way, SpaceMACS identifies common substructures
even if they cross fragment borders. We applied SpaceMACS on three
commercial fragment spaces searching for the 150 000 most similar
analogs to a glucosyl­transferase binder from literature. We
were able to find almost all building blocks used for the synthesis
of the 90 listed analogs and a plethora of additional results. SpaceMACS
is the missing link to enable rational drug discovery on make-on-demand
combinatorial catalogs. No matter whether initial compound suggestions
come from a de novo design, an AI-based compound generation, or a
medicinal chemist’s drawing board, the method gives access
to the structurally closest chemically available analogs in seconds
to at most minutes.