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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\u2013glucoamylase and to \u03b1-amylase. Both the conventional mixed-probe SILCS workflow and a modified neutral\u2013charged protocol were evaluated using the CHARMM additive and Drude polarizable force fields. FragMap convergence analysis demonstrated reliable sampling across all systems, with the neutral\u2013charged 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\u2013charged 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.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01733","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:02:22Z","timestamp":1790766142000},"page":"12969-12988","source":"Crossref","is-referenced-by-count":1,"title":["Computer-Aided\nDrug Design for Type 2 Diabetes-Related\nEnzymes: SILCS-Based Fragment Mapping, Pharmacophore Modeling, and\nVirtual Screening"],"prefix":"10.1021","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2631-4990","authenticated-orcid":true,"given":"Esmat","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Virginia Tech , , , ,","place":["Blacksburg, Virginia, United States, 24061"]},{"name":"Virginia Tech , , , ,","place":["Blacksburg, Virginia, United States, 24061"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6661-8653","authenticated-orcid":true,"given":"Justin A.","family":"Lemkul","sequence":"additional","affiliation":[{"name":"Virginia Tech , , , ,","place":["Blacksburg, Virginia, United States, 24061"]},{"name":"Virginia Tech , , , ,","place":["Blacksburg, Virginia, United States, 24061"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"issue":"17","key":"2026101208070526200_cit1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21176275","article-title":"Pathophysiology\nof type 2 diabetes mellitus","volume":"21","author":"Galicia-Garcia","year":"2020","journal-title":"Int. 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