Computer-Aided Drug Design for Type 2 Diabetes-Related Enzymes: SILCS-Based Fragment Mapping, Pharmacophore Modeling, and Virtual Screening

Abstract

Highly charged enzyme active sites present a challenge for conventional Site Identification by Ligand Competitive Saturation (SILCS) because strongly interacting charged probes can suppress sampling of neutral fragments, while additive force fields do not explicitly account for electronic polarization. To address these limitations, we developed a comprehensive SILCS-based computational framework and applied it to the N-terminal and C-terminal domains of maltase–glucoamylase and to α-amylase. Both the conventional mixed-probe SILCS workflow and a modified neutral–charged protocol were evaluated using the CHARMM additive and Drude polarizable force fields. FragMap convergence analysis demonstrated reliable sampling across all systems, with the neutral–charged protocol alleviating competitive exclusion effects observed in conventional simulations. In highly charged catalytic pockets, the conventional workflow was dominated by cationic probe sampling, suppressing neutral and hydrogen-bonding features. Separation of charged and neutral probes restored chemically diverse interaction patterns, improving representation of hydrogen-bond donor, acceptor, and hydrophobic hotspots. Incorporation of the Drude polarizable force field further enhanced resolution of electrostatic and hydrogen-bonding interactions. Energy-based SILCS-Pharmacophore models were generated from the neutral–charged FragMaps and used for virtual screening of the Maybridge and ChemBridge libraries. Hits were refined using SILCS-MC, ranked by ligand grid free energy (LGFE) and ligand efficiency (LE), and analyzed through scaffold- and fingerprint-based clustering. Integration of energetic and structural analyses enabled identification of favorable scaffolds and functional group patterns associated with strong binding efficiency. Together, this study develops, evaluates, and applies an improved SILCS workflow to three enzymes associated with type 2 diabetes (T2D), demonstrating its utility for characterizing highly charged active sites and prioritizing candidate inhibitors.