Computational Investigation of Montelukast and Its Structural Derivatives for Binding Affinity to Dopaminergic and Serotonergic Receptors: Insights from a Comprehensive Molecular Simulation

Abstract

Background/Objectives: Montelukast (MLK), a leukotriene receptor antagonist, has been associated with neuropsychiatric side effects. This study aimed to rationally modify MLK's structure to reduce these risks by optimizing its interactions with dopamine D2 (DRD2) and serotonin 5-HT1A receptors using computational molecular simulation techniques. Methods: A library of MLK derivatives was designed and screened using structural similarity analysis, molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and ADME-Tox predictions. Structural similarity analysis, based on Tanimoto coefficient fingerprinting, compared MLK derivatives to known neuropsychiatric drugs. Docking was performed to assess initial receptor binding, followed by 100 ns MD simulations to evaluate binding stability. MM/PBSA calculations quantified binding affinities, while ADME-Tox profiling predicted pharmacokinetic and toxicity risks. Results: Several MLK derivatives showed enhanced DRD2 and 5-HT1A binding. MLK_MOD-42 and MLK_MOD-43 emerged as the most promising candidates, exhibiting MM/PBSA binding free energies of -31.92 ± 2.54 kcal/mol and -27.37 ± 2.22 kcal/mol for DRD2 and -30.22 ± 2.29 kcal/mol and -28.19 ± 2.14 kcal/mol for 5-HT1A, respectively. Structural similarity analysis confirmed that these derivatives share key pharmacophoric features with atypical antipsychotics and anxiolytics. However, off-target interactions were not assessed, which may influence their overall safety profile. ADME-Tox analysis predicted improved oral bioavailability and lower neurotoxicity risks. Conclusions: MLK_MOD-42 and MLK_MOD-43 exhibit optimized receptor interactions and enhanced pharmacokinetics, suggesting potential neuropsychiatric applications. However, their safety and efficacy remain to be validated through in vitro and in vivo studies. Until such validation is performed, these derivatives should be considered as promising candidates with optimized receptor binding rather than confirmed safer alternatives.

Keywords: 5-HT1A; D2 dopamine; molecular docking; molecular dynamics; montelukast; pharmacophore modeling.

Figure 1

Structural alignment and similarity analysis of Montelukast and its modifications. (A) Superimposition of Montelukast modifications with the native structure (backbone representation). (B) A 3D superimposition of Montelukast modifications with the native structure. Different colors represent different Montelukast modifications, allowing visual comparison of structural variations. (C) Pairwise similarity correlation heatmap, depicting the structural similarity scores between Montelukast and its modified derivatives. Higher similarity scores indicate minimal structural deviation, while lower scores suggest significant modifications. (D) Similarity score distribution plot, providing a comparative analysis of the degree of structural resemblance between each modified derivative and the native Montelukast. Purple colors indicate the similarity of each modification compared to the native structure of Montelukast.

Figure 2

Comparative 3D binding poses of Haloperidol, Montelukast, and its best-performing modifications in the DRD2 ligand-binding domain (LBD). (A) A 3D binding pose of Haloperidol (standard antagonist) showing deep placement within the LBD. (B) Binding pose of native Montelukast (MLK_DDR2) positioned more peripherally in the receptor pocket. (C) The binding pose of MLK_MOD-43 within the DRD2 binding pocket demonstrates a deeper placement similar to Haloperidol. (D) Binding pose of MLK_MOD-42, highlighting its strong receptor engagement and forming a hydrogen bond with Ser409. (E) Binding pose of MLK_MOD-22, showing notable ligand–receptor interactions and forming a hydrogen bond with Ser409. Hydrogen bonds are represented by blue dashed lines.

Figure 3

Two-dimensional interaction maps of Haloperidol, Montelukast (MLK), and its best-performing modifications with the DRD2 binding site. (A) Haloperidol_DRD2 complex. (B) MLK_DDR2 complex. (C) MLK_MOD-43_DRD2 complex. (D) MLK_MOD-42_DRD2 complex. (E) MLK_MOD-22_DRD2 complex. The interaction types are color-coded as follows: hydrogen bonds (bright green), van der Waals interactions (pale green), Pi–Alkyl (pink), Pi–Sigma (purple), Pi–Sulfur (orange), and halogen interactions (bright blue).

Figure 4

Comparative 3D binding poses of Buspirone, Montelukast, and its best-performing modifications in the 5-HT1A ligand-binding domain (LBD). (A) Buspirone_5-HT1A complex. (B) MLK_5-HT1A complex. (C) MLK_MOD-42_5-HT1A complex. (D) MLK_MOD-21_5-HT1A complex. (E) MLK_MOD-43_5-HT1A complex. Hydrogen bonds are represented by blue dashed lines.

Figure 5

Two-dimensional interaction maps of Buspirone, Montelukast (MLK), and its best-performing modifications with the 5-HT1A binding site. (A) Buspirone_5-HT1A complex. (B) MLK_5-HT1A complex. (C) MLK_MOD-42_5-HT1A complex. (D) MLK_MOD-21_5-HT1A complex. (E) MLK_MOD-43_5-HT1A complex. The interaction types are color-coded as follows: hydrogen bonds (bright green), van der Waals interactions (pale green), Pi–Alkyl (pink), Pi–Sigma (purple), and Pi–Sulfur (orange).

Figure 6

Molecular docking simulation results. (A) Binding affinity values (kcal/mol) of the top 15 Montelukast (MLK) modifications docked to the active site of DRD2. (B) Binding affinity values (kcal/mol) of the top 15 MLK modifications docked to the active site of 5-HT1A. (C) Correlation matrix depicting the relationship between binding energy (kcal/mol) and individual energy components (van der Waals, electrostatic, desolvation, and restraints violation energies) for MLK modifications docked to DRD2. (D) Correlation matrix illustrating the relationship between binding energy (kcal/mol) and individual energy components for MLK modifications docked to 5-HT1A. Correlation values range from −1 to 1, where 1 represents a perfect positive correlation, −1 indicates a perfect negative correlation, and 0 denotes no correlation.

Figure 7

Three-dimensional scatter plot of selected principal components for MLK modifications docked to DRD2 and 5-HT1A. (A) Three-dimensional scatter plot illustrating the relationship between predicted activity, binding affinity, and statistical docking score for MLK modification-DRD2 complexes. (B) Three-dimensional scatter plot representing the correlation between predicted activity, binding affinity, and statistical docking score for MLK modification-5-HT1A complexes. Each point corresponds to a molecule and is colored according to its activity.

Figure 8

Root mean square fluctuation (RMSF) profiles of ligand–receptor complexes. (A) RMSF profile of Montelukast (MLK) modifications complexed with DRD2, compared to Haloperidol (standard antagonist). (B) RMSF profile of MLK modifications complexed with 5-HT1A, compared to Buspirone (standard agonist).

Figure 9

Three-dimensional pharmacophore modeling. (A) Haloperidol_DRD2 complex. (B) MLK_DDR2 complex. (C) MLK_MOD-43_DRD2 complex. (D) MLK_MOD-42_DRD2 complex. (E) MLK_MOD-22_DRD2 complex. Yellow spheres indicate hydrophobic interactions, green arrows represent hydrogen bond donors, and red arrows signify hydrogen bond acceptors.

Figure 10

Three-dimensional pharmacophore modeling. (A) Buspirone_5-HT1A complex. (B) MLK_5-HT1A complex. (C) MLK_MOD-42_5-HT1A complex. (D) MLK_MOD-21_5-HT1A complex. (E) MLK_MOD-43_5-HT1A complex. Yellow spheres indicate hydrophobic interactions, green arrows represent hydrogen bond donors, and red arrows signify hydrogen bond acceptors.