Macrocyclization of pharmaceutical compounds plays an increasing role in drug discovery. Macrocycles can provide several advantages such as favorable drug-like properties, increased selectivity and improved binding affinity. The assessment of potential macrocyclizations is a challenging computational problem. Linkers should be synthetically feasible, not too flexible, have a sensible conformation and be pharmacophorically compatible with the active site. Few computational strategies are available for this task, so most design is done ad hoc. Here we present a modification of our existing bioisostere searching tool, Spark, to this problem. Traditional bioisostere searches specify a fragment to replace in the starting ligand, and look for similar fragments in a database. Instead, Spark assesses bioisosteric replacements in product space, which allows more complex experiments. A modification of the Spark scoring method to include similarity to other ligands known to bind in the region of the linker, as well as constraints from the protein active site and known pharmacophoric requirements, allows sensible ranking of potential linkers. Here we present case studies of designing macrocyclization strategies for BRD4 and for Orexin 2. The Spark algorithms enable a rapid assessment of the ideal linker length and suggested chemistry for each cyclization option.

Rapid and accessible in silico macrocycle design

256th National Meeting (2018)

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