A Multiscale Computational Strategy for the Rational Design of Novel Aminoguanidine‐Based Chitinase‐h Inhibitors

ABSTRACT Chitinase‐h from Ostrinia furnacalis ( Of Chi‐h) is a promising target for eco‐friendly pest control due to its role in larval development. Here, 32 aminoguanidine derivatives were investigated as Of Chi‐h inhibitors via an integrated computational approach to explore structure‐activity relationships (SARs) and screen for potential candidates. Despite the small dataset ( n = 32), the 3D‐QSAR models showed robust predictive performance (CoMFA: q 2 = 0.563, r 2 = 0.977; CoMSIA: q 2 = 0.543, r 2 = 0.989), revealing that steric, electrostatic, and hydrophobic fields significantly influence activity. Molecular docking highlighted key interactions with Trp268, Glu308, and Asp384, while pharmacophore modeling highlighted hydrogen bond features as essential for binding. Virtual screening of a 40,000‐compound library yielded four hits; S02 and S03 exhibited comparatively favorable dynamic behavior in 100 ns MD simulations and high binding affinity (Δ G bind : −90.36 and −71.60 kJ/mol). Newly designed molecules D04 and D05 also demonstrated superior predicted activity and stability. This integrated multiscale strategy therefore not only provides valuable insights for designing potent Of Chi‐h inhibitors but also proposes promising candidate molecules that warrant future experimental synthesis and biological profiling.