Kinase Inhibitor Data Modeling and de Novo Inhibitor Design with Fragment Approaches

publication · 7 years ago
by Ian A. Watson, Michal Vieth, Jon Erickson, Jibo Wang, Yue Webster, Mary Mader, Richard Higgs (Eli Lilly and Co.)
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A reconstructive approach based on computational fragmentation of existing inhibitors and validated kinase potency models to recombine and create “de novo” kinase inhibitor small molecule libraries is described. The screening results from model selected molecules from the corporate database and seven computationally derived small molecule libraries were used to evaluate this approach. Specifically, 1895 model selected database molecules were screened at 20 μM in six kinase assays and yielded an overall hit rate of 84%. These models were then used in the de novo design of seven chemical libraries consisting of 20−50 compounds each. Then 179 compounds from synthesized libraries were tested against these six kinases with an overall hit rate of 92%. Comparing predicted and observed selectivity profiles serves to highlight the strengths and limitations of the methodology, while analysis of functional group contributions from the libraries suggest general principles governing binding of ATP competitive compounds.
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