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<p>Butyrylcholinesterase (BuChE) is a key enzyme implicated in the pathogenesis of Alzheimer’s disease (AD), and its inhibition represents a promising therapeutic strategy for disease management. Among various inhibitor classes, carbamate derivatives have attracted significant attention due to their pseudo-irreversible inhibition mechanism and favorable pharmacological profiles, making them valuable scaffolds in anti-Alzheimer drug discovery. In this study, a dataset of 205 carbamate derivatives was carefully compiled from reliable peer-reviewed literature, and QSAR modeling was performed for the first time on this dataset. Quantitative structure–activity relationship (QSAR) models were constructed to predict the BuChE inhibitory activity (pIC50) employing Monte Carlo optimization within the CORAL-2023 software framework. Hybrid optimal descriptors derived from SMILES notation and hydrogen-suppressed molecular graphs were utilized. Sixty models were developed across four random splits using four distinct target functions (TF0–TF3), among which the TF3-based models exhibited superior statistical performance (validation R<sup>2</sup> ranging from 0.80 to 0.86, Q<sup>2</sup> between 0.78 and 0.84, and RMSE values from 0.45 to 0.54). The mechanistic interpretation of the model showed that the increasing SMILES-based descriptors correspond to key pharmacophoric regions of the BuChE active site, including the PAS, acyl pocket, and catalytic triad. These correlations confirm that aromatic, hydrophobic, and branched fragments enhance inhibitory activity through π–π interactions, hydrophobic anchoring, and optimal orientation toward Ser198.</p>

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SMILES-based QSAR analysis of carbamate derivatives targeting butyrylcholinesterase

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Negin Latifi,
  • Shahin Ahmadi,
  • Shahram Lotfi,
  • Saeid Akbarzadeh Kolahi

摘要

Butyrylcholinesterase (BuChE) is a key enzyme implicated in the pathogenesis of Alzheimer’s disease (AD), and its inhibition represents a promising therapeutic strategy for disease management. Among various inhibitor classes, carbamate derivatives have attracted significant attention due to their pseudo-irreversible inhibition mechanism and favorable pharmacological profiles, making them valuable scaffolds in anti-Alzheimer drug discovery. In this study, a dataset of 205 carbamate derivatives was carefully compiled from reliable peer-reviewed literature, and QSAR modeling was performed for the first time on this dataset. Quantitative structure–activity relationship (QSAR) models were constructed to predict the BuChE inhibitory activity (pIC50) employing Monte Carlo optimization within the CORAL-2023 software framework. Hybrid optimal descriptors derived from SMILES notation and hydrogen-suppressed molecular graphs were utilized. Sixty models were developed across four random splits using four distinct target functions (TF0–TF3), among which the TF3-based models exhibited superior statistical performance (validation R2 ranging from 0.80 to 0.86, Q2 between 0.78 and 0.84, and RMSE values from 0.45 to 0.54). The mechanistic interpretation of the model showed that the increasing SMILES-based descriptors correspond to key pharmacophoric regions of the BuChE active site, including the PAS, acyl pocket, and catalytic triad. These correlations confirm that aromatic, hydrophobic, and branched fragments enhance inhibitory activity through π–π interactions, hydrophobic anchoring, and optimal orientation toward Ser198.