Computational Method to Evaluate Progress in Lead Optimization

In medicinal chemistry,
lead optimization is a critically important
task and a highly empirical process, largely driven by chemical knowledge
and intuition. Only very few approaches are available to guide and
evaluate optimization efforts. It is often very difficult to understand
when a compound series is exhausted and the generation of additional
analogs unlikely to yield further progress toward potent and efficacious
candidates. Rationalizing lead optimization remains an essentially
unsolved problem. Herein, we introduce a new computational method
to aid in evaluating whether sufficient numbers of analogs have been
made and further progress is unlikely. The approach integrates the
assessment of chemical saturation and structure–activity relationship
progression of compound series. Easy-to-calculate scores characterize
evolving analog series and identify candidates with high or low priority
for further chemical exploration.