Design and Evaluation of Indole-Based Schiff Bases as α-Glucosidase Inhibitors: CNN-Enhanced Docking, MD Simulations, ADMET Profiling, and SAR Analysis

Type 2 diabetes mellitus (T2DM) remains a global health challenge, prompting the development of novel α-glucosidase inhibitors (AGIs) to regulate postprandial hyperglycemia. This study reports the design, synthesis, and evaluation of indole-based Schiff base derivatives (<b>4a</b>-<b>j</b>) bearing a fixed methoxy group at the C<sub>5</sub> position. This substitution was strategically introduced to enhance lipophilicity, electronic delocalization, and π-stacking within the enzyme active site. Among the series, compound <b>4g</b> (3-bromophenyl) exhibited the highest inhibitory activity (IC<sub>50</sub> = 10.89 µM), outperforming the clinical reference acarbose (IC<sub>50</sub> = 48.95 µM). The mechanism was supported by in silico analyses, such as the Density Functional Theory (DFT), molecular electrostatic potential (MEP) mapping, and molecular dynamics simulations, and CNN-based docking revealed that <b>4g</b> engages in stable hydrogen bonding and π-π interactions with key residues (Asp327, Asp542, and Phe649), suggesting a potent and selective mode of inhibition. In silico ADMET predictions indicated favorable pharmacokinetic properties. Together, these results establish C<sub>5</sub>-methoxy substitution as a viable strategy to enhance α-glucosidase inhibition in indole-based scaffolds.

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Journal
Molecules
Published
2025-09-08
DOI
https://doi.org/10.3390/molecules30173651
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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article

Design and Evaluation of Indole-Based Schiff Bases as α-Glucosidase Inhibitors: CNN-Enhanced Docking, MD Simulations, ADMET Profiling, and SAR Analysis

Balasaheb K. Ghotekar, Fabiola Hernández-Rosas, Shripad V. Bhagwat, J. Óscar C. Jiménez‐Halla et al.
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Computational Drug Discovery Methods
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article

Design and Evaluation of Indole-Based Schiff Bases as α-Glucosidase Inhibitors: CNN-Enhanced Docking, MD Simulations, ADMET Profiling, and SAR Analysis

Balasaheb K. Ghotekar, Fabiola Hernández-Rosas, Shripad V. Bhagwat, J. Óscar C. Jiménez‐Halla, Enrique Delgado‐Alvarado, Irving David Pérez-Landa, Vivek D. Bobade, Tushar Janardan Pawar, Abraham Vidal‐Limon, Sachin V. Patil
article en
1 citations

Abstract

Type 2 diabetes mellitus (T2DM) remains a global health challenge, prompting the development of novel α-glucosidase inhibitors (AGIs) to regulate postprandial hyperglycemia. This study reports the design, synthesis, and evaluation of indole-based Schiff base derivatives (<b>4a</b>-<b>j</b>) bearing a fixed methoxy group at the C<sub>5</sub> position. This substitution was strategically introduced to enhance lipophilicity, electronic delocalization, and π-stacking within the enzyme active site. Among the series, compound <b>4g</b> (3-bromophenyl) exhibited the highest inhibitory activity (IC<sub>50</sub> = 10.89 µM), outperforming the clinical reference acarbose (IC<sub>50</sub> = 48.95 µM). The mechanism was supported by in silico analyses, such as the Density Functional Theory (DFT), molecular electrostatic potential (MEP) mapping, and molecular dynamics simulations, and CNN-based docking revealed that <b>4g</b> engages in stable hydrogen bonding and π-π interactions with key residues (Asp327, Asp542, and Phe649), suggesting a potent and selective mode of inhibition. In silico ADMET predictions indicated favorable pharmacokinetic properties. Together, these results establish C<sub>5</sub>-methoxy substitution as a viable strategy to enhance α-glucosidase inhibition in indole-based scaffolds.

MoleculesVol. 30(17)
Universidad de Guanajuato (MX), Universidad Veracruzana (MX), Maharashtra University of Health Sciences (IN), Autonomous University of Queretaro (MX), Universidad Anáhuac Querétaro (MX), Instituto Tecnológico de Veracruz (MX), Instituto de Ecología (MX)
Instituto Potosino de Investigación Científica y Tecnológica, National Supercomputing Center, Korea Institute of Science and Technology Information
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.50
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