Dynamic combinatorial and protein-templated click chemistry in medicinal chemistry

We have highlighted throughout this thesis that fragment-based drug design (FBDD) and structure-based drug design (SBDD) still constitute a number of challenges such as the risk associated with de novo SBDD and are time-consuming as they involve synthesis and validation of the binding mode of each derivative in the fragment/hit-optimization cycle. To overcome these hurdles, we combined FBDD or de novo SBDD projects with dynamic combinatorial chemistry (DCC) or protein-templated click chemistry (PTCC) to render the identification/optimization of hits/leads more efficient, using the aspartic protease endothiapepsin as a model system. The main achievements described in this thesis are: 1) the development of a powerful technique that combines de novo SBDD and DCC for the rapid identification of novel hits, 2) the development of an efficient approach that combines fragment linking and DCC to accelerate hit-to-lead optimization, 3) optimization of an initial hit, 4) development of a technique that combines fragment growing and DCC for the rapid optimization of a fragment, and 5) the development of a method that combines fragment linking/optimization and PTCC to accelerate the hit-identification process using the aspartic protease endothiapepsin as a model enzyme. Based on these accomplishments, it can be concluded that several combinations of computational and analytical techniques are synergistic and facilitate the identification/optimization of hits/leads for the aspartic protease endothiapepsin