Beyens_2024_J.Chem.Inf.Model_64_7650

We present our efforts in computational drug design against dipeptidyl peptidase 4 (DPP4), DPP8 and DPP9. We applied cosolvent molecular dynamics (MD) simulations to these three protein targets of interest. Our primary motivation is the growing interest in DPP8 and DPP9 as emerging drug targets. Due to the high similarity between DPP4, DPP8 and DPP9, DPP4 was also included in these analyses. The cosolvent molecular dynamics simulations reproduce key ligand binding features and known binding pockets, while also highlighting interesting fragment positions for future ligand optimization. The resulting fragment maps from the cosolvent molecular dynamics are freely available for use in future research (https://github.com/UAMC-Olivier/DPP489_cosolvent_MD/). Detailed instructions for easy visualization of the fragment maps are provided, ensuring that the results are usable by both computational and medicinal chemists. Additionally, we used the fragment maps to search for the binding pockets with significant potential using an algorithmic approach combining top fragment locations. To discover novel binding scaffolds, a limited pharmacophore screening was performed, where the pharmacophores were based on the analyses of the cosolvent simulations. Unfortunately, inhibitory potencies were in the higher micromolar range, but we optimized the resulting scaffolds in silico using relative binding free energy calculations for future inhibitor design and synthesis.