Development of New Imidazole Derivatives using 3D-QSAR Modeling and Molecular Docking

Priyanka Yadav¹*, Neeraj Upmanyu²

¹Research Scholor Sanjeev Agrawal Global Educational University (SAGE),

Sahara Bypass Road, Katara Hill Extension, Bhopal, (M.P.) 462023.

²Professor and Pro-Vice Chancellor, Sanjeev Agrawal Global Educational University (SAGE),

Sahara Bypass Road, Katara Hill Extension, Bhopal, (M.P.) 462023.

*Corresponding Author E-mail: priyankaya.9889@gmail.com

ABSTRACT:

Clinical investigations have evaluated the medicinal value of a variety of imidazole-containing substances for a number of disease-related conditions. The quick development of medicinal chemistry revolving around imidazoles indicates that molecules produced from imidazoles have promising and possible therapeutic uses in the treatment of terminal illnesses. In contrast to other heterocyclic rings, three carbons make up the imidazole scaffold, along with two nitrogen-ensuing electronic-rich properties that enable it to connect with a wide range of proteins, enzymes, and receptors with ease. In this work, a number of imidazole derivatives exhibiting antifungal, anticonvulsant, and anti-inflammatory properties underwent docking at the molecular level, followed by quantitative structure activity relationship (QSAR) exploration in an effort to identify the optimal physicochemical properties of putative human lanosterol 14α-demethylase inhibitors. The docking studies indicated that compounds exhibited good binding affinity with the receptor proteins of -10.4, -8.7, and -10.9 kcal/mol with 1E9X, 1EOU, and 4COX, respectively. The QSAR model highlighted the significance of steric, electrostatic, and hydrophobic features through their contours in the best-developed model.

KEYWORDS: Antifungal, Anticonvulsant, Anti-inflammatory, Molecular docking, 3D QSAR, Contours.

1. INTRODUCTION: 

Imidazoles, five-membered aromatic rings with two nitrogen atoms, play a crucial role in biology, pharmacology, and medicinal chemistry¹. Their unique structure enables various interactions, making them key components in drug design. Since their discovery in the 1840s,² imidazoles have been recognized for their potential in treating a wide range of conditions, including inflammation, cancer, and infections^3-9.

These compounds are found in essential biological molecules like biotin, cobalamin, and nucleotides,^10-13 and their derivatives are widely used as antifungal agents, exemplified by drugs such as ketoconazole and miconazole^14-17.

Recent research has focused on developing imidazole derivatives as novel anticonvulsants and anti-inflammatory agents. Due to the need for safer and more effective treatments, particularly in anti-epileptic drugs, understanding the structural activity relationships and protein-ligand interactions of imidazoles is crucial. These efforts are driven by the limitations of current medications, like NSAIDs and corticosteroids, which often have undesirable side effects¹⁸. Imidazole-based compounds hold promise for advancing medicinal chemistry, offering potential for safer, more effective therapies across various disease areas¹⁹.

2. METHOD:

2.Mo1ecular Docking:

AutoDock vina was used for docking studies. This application uses both computer-generated annealing and genomic algorithms to identify suitable dockings in a protein-binding spot, given a ligand fragment in a random conformation, alignment, and location²⁰. The dataset of 104 ligand molecules was drawn, minimized, and proceeded with for molecular docking and virtual screening. The structural representation of imidazole scaffolds with different substitutions is presented in Table 1. Biovia Discovery Studio Visualiser 2020 was used for protein preparation. Protein preparation involved the participation of different features of Discovery Studio. The molecular docking was accomplished using the MGL tool of AutoDock Vina. The commands in the configuration text (conf.txt) or log file (log.txt) initiated the calculations and gave information about forces generated by AutoDock Vina. The refined results were obtained, showing different binding poses of ligands with their affinity (kcal/mol) and rsmd values. The output files were visualized in the Discovery Studio interface to study the ligand-receptor interaction of the best docked molecules.

2.2. 3D QSAR studies on designed, synthesized and biologically evaluated imidazole derivatives:

Dataset:

The in vitro cellular statistics of a sequence of 50 imidazoles as human lanosterol 14α-demethylase inhibitors were used for the study. The elementary configurations of the imidazole scaffold and its innumerable substituents are recorded in Table 1. The 14α-demethylase inhibitory doings were articulated as IC50. The IC50 values of the synthesized 50 compounds are converted into pIC50 (Table 2). The data set was alienated arbitrarily into training and test sets by bearing in mind that 70% of entire molecules were in the training category and 30% were in the test category. Forty-three molecules creating the training category were cast off to spawn a pharmacophore model and prophecy of the activity of the test category (07 compounds). ligands were employed as a technique to authenticate the projected models.

Table 1: The molecular structure of 104 ligands with different substituting groups at R¹, R² and R³ position.

Compd Code	R^1	R^2	R^3	Compd Code	R^1	R^2	R^3
1	-phenyl	-phenyl	H	53	-(4-chloro) phenyl	-phenyl	H
2		-(4-CH3) phenyl	H	54		-(4-CH3) phenyl	H
3		-(4-OCH3) phenyl	H	55		-(4-OCH3) phenyl	H
4		-(4-F) phenyl	H	56		-(4-F) phenyl	H
5		-(4-Cl) phenyl	H	57		-(4-Cl) phenyl	H
6		-(4-Br) phenyl	H	58		-(4-Br) phenyl	H
7		-(4-NO2) phenyl	H	59		-(4-NO2) phenyl	H
8		-(3-CH3) phenyl	H	60		-(3-CH3) phenyl	H
9		-(3-OCH3) phenyl	H	61		-(3-OCH3) phenyl	H
10		-(3-F) phenyl	H	62		-(3-F) phenyl	H
11		-(3-Cl) phenyl	H	63		-(3-Cl) phenyl	H
12		-(3-Br) phenyl	H	64		-(3-Br) phenyl	H
13		-(3-NO2) phenyl	H	65		-(3-NO2) phenyl	H
14	-(4-methyl) phenyl	-phenyl	H	66	-(4-bromo) phenyl	-phenyl	H
15		-(4-CH3) phenyl	H	67		-(4-CH3) phenyl	H
16		-(4-OCH3) phenyl	H	68		-(4-OCH3) phenyl	H
17		-(4-F) phenyl	H	69		-(4-F) phenyl	H
18		-(4-Cl) phenyl	H	70		-(4-Cl) phenyl	H
19		-(4-Br) phenyl	H	71		-(4-Br) phenyl	H
20		-(4-NO2) phenyl	H	72		-(4-NO2) phenyl	H
21		-(3-CH3) phenyl	H	73		-(3-CH3) phenyl	H
22		-(3-OCH3) phenyl	H	74		-(3-OCH3) phenyl	H
23		-(3-F) phenyl	H	75		-(3-F) phenyl	H
24		-(3-Cl) phenyl	H	76		-(3-Cl) phenyl	H
25		-(3-Br) phenyl	H	77		-(3-Br) phenyl	H
26		-(3-NO2) phenyl	H	78		-(3-NO2) phenyl	H
27	-(4-methoxy) phenyl	-phenyl	H	79	-(4-nitro) phenyl	-phenyl	H
28		-(4-CH3) phenyl	H	80		-(4-CH3) phenyl	H
29		-(4-OCH3) phenyl	H	81		-(4-OCH3) phenyl	H
30		-(4-F) phenyl	H	82		-(4-F) phenyl	H
31		-(4-Cl) phenyl	H	83		-(4-Cl) phenyl	H
32		-(4-Br) phenyl	H	84		-(4-Br) phenyl	H
33		-(4-NO2) phenyl	H	85		-(4-NO2) phenyl	H
34		-(3-CH3) phenyl	H	86		-(3-CH3) phenyl	H
35		-(3-OCH3) phenyl	H	87		-(3-OCH3) phenyl	H
36		-(3-F) phenyl	H	88		-(3-F) phenyl	H
37		-(3-Cl) phenyl	H	89		-(3-Cl) phenyl	H
38		-(3-Br) phenyl	H	90		-(3-Br) phenyl	H
39		-(3-NO2) phenyl	H	91		-(3-NO2) phenyl	H
40	-(4-fluoro) phenyl	-phenyl	H	92	-(4-hydroxy) phenyl	-phenyl	H
41		-(4-CH3) phenyl	H	93		-(4-CH3) phenyl	H
42		-(4-OCH3) phenyl	H	94		-(4-OCH3) phenyl	H
43		-(4-F) phenyl	H	95		-(4-F) phenyl	H
44		-(4-Cl) phenyl	H	96		-(4-Cl) phenyl	H
45		-(4-Br) phenyl	H	97		-(4-Br) phenyl	H
46		-(4-NO2) phenyl	H	98		-(4-NO2) phenyl	H
47		-(3-CH3) phenyl	H	99		-(3-CH3) phenyl	H
48		-(3-OCH3) phenyl	H	100		-(3-OCH3) phenyl	H
49		-(3-F) phenyl	H	101		-(3-F) phenyl	H
50		-(3-Cl) phenyl	H	102		-(3-Cl) phenyl	H
51		-(3-Br) phenyl	H	103		-(3-Br) phenyl	H
52		-(3-NO2) phenyl	H	104		-(3-NO2) phenyl	H

Table 2: The IC₅₀ and Pic₅₀ values of 50 synthesized substituted imidazole derivatives tabulated in form of dataset for QSAR study.

Sr. No.	Ligands Name	IC50 (μM)	pIC50 value	Sr. No.	Ligands Name	IC50 (μM)	pIC50 value
1.	1e9x_47_uff_E=532.76	0.021	7.678	26.	1e9x_91_uff_E=489.55	1.44	5.842
2.	1e9x_52_uff_E=582.54	0.035	7.456	27.	1e9x_7_uff_E=569.84	1.6	5.796
3.	1e9x_15_uff_E=557.90	0.14	6.854	28.	1e9x_34_uff_E=542.70	1.7	5.770
4.	1e9x_21_uff_E=475.26	0.21	6.678	29.	1e9x_37_uff_E=540.83	1.8	5.745
5.	1e9x_18_uff_E=553.75	0.27	6.569	30.	1e9x_65_uff_E=582.23	1.8	5.745
6.	1e9x_58_uff_E=598.66	0.32	6.495	31.	1e9x_100_uff_E=544.52	1.9	5.721
7.	1e9x_23_uff_E=473.51	0.49	6.310	32.	1e9x_77_uff_E=548.22	1.93	5.714
8.	1e9x_41_uff_E=517.98	0.55	6.260	33.	1e9x_99_uff_E=530.25	2.1	5.678
9.	1e9x_85_uff_E=609.80	0.60	6.222	34.	1e9x_92_uff_E=549.20	2.1	5.678
10.	1e9x_53_uff_E=474.59	0.62	6.208	35.	1e9x_93_uff_E=517.88	2.1	5.678
11.	1e9x_56_uff_E=566.69	0.69	6.161	36.	1e9x_102_uff_E=536.45	2.52	5.599
12.	1e9x_101_uff_E=530.41	0.72	6.143	37.	1e9x_1_uff_E=532.78	2.79	5.554
13.	1e9x_8_uff_E=532.20	0.72	6.143	38.	1e9x_29_uff_E=617.51	2.8	5.553
14.	1e9x_10_uff_E=531.61	0.76	6.119	39.	1e9x_31_uff_E=637.66	4.0	5.398
15.	1e9x_73_uff_E=554.65	0.80	6.097	40.	1e9x_48_uff_E=541.36	4.1	5.387
16.	1e9x_75_uff_E=554.70	0.84	6.076	41.	1e9x_71_uff_E=511.56	4.12	5.385
17.	1e9x_95_uff_E=518.67	0.87	6.060	42.	1e9x_19_uff_E=557.13	4.2	5.377
18.	1e9x_20_uff_E=487.28	0.92	6.036	43.	1e9x_4_uff_E=519.01	4.6	5.337
19.	1e9x_22_uff_E=490.76	0.96	6.018	44.	1e9x_11_uff_E=535.47	4.9	5.310
20.	1e9x_59_uff_E=489.62	1.07	5.971	45.	1e9x_78_uff_E=736.58	5.4	5.268
21.	1e9x_79_uff_E=572.26	1.07	5.971	46.	1e9x_14_uff_E=486.87	5.9	5.229
22.	1e9x_46_uff_E=495.82	1.15	5.939	47.	1e9x_80_uff_E=602.35	6.4	5.194
23.	1e9x_68_uff_E=593.47	1.30	5.886	48.	1e9x_72_uff_E=505.00	8.3	5.080
24.	1e9x_90_uff_E=544.52	1.31	5.883	49.	1e9x_81_uff_E=569.07	9.8	5.009
25.	1e9x_83_uff_E=583.14	1.40	5.854	50	1e9x_47_uff_E=532.76	10.28	4.988

Pharmacophore hypothesis generation:

The common pharmacophore feature generation means is beneficial to collect the chemical parameters pooled by a set of ligands and to express out the common features by the alliance of ligands with the best possible pharmacophore. Pharmacophore modeling was done using the HypoGen algorithm in Discover Studio. The idea of the common pharmacophore hypothesis (CPH) suggested that all compounds shared at least five locations. Moreover, at least one hypothesis needs to be identified and successfully scored; hence, the finest CPH was preferred based on the survival score. Default values for survival, site, vector, and volume were used to score the hypotheses.

Phase methodology for QSAR model building and statistical analysis:

PHASE version 3.0, which was integrated into the Maestro 8.5 modeling program from Schrodinger, Molecular Modelling Interface Inc., LLC, New York, NY, USA, was used to conduct the 3D-QSAR experiments. The training (43) and test (7) sets of the dataset were effectively divided for analysis using the rational segregation approach. A random division was used to produce the model that fit the data the best. With a maximum PLS factor of 7 and 1 grid spacing, the model was constructed to choose the best-fitting hypothesis and assign a score. To choose and validate the optimum model, statistical metrics such as R2, R2CV, stability, RMSE, Q2, and Pearson r factors were taken into account. Ultimately, the outcomes were displayed using various Gaussian characteristics. The visualization of the QSAR results later on finally aided in the dataset's thrust structure optimization.

3. RESULT AND DISCUSSION:

3.1. Molecular docking study:

Molecular docking readings were accomplished to pattern the binding locations and interactions that ensue amongst the designed complexes and macromolecules. The values of binding energy for the designed molecules are apprehended in Table 3. The binding affinity of the well-established marketed antifungal drugs miconazole and ketoconazole is -6.194kcal/mol (Figs. 1a and 1b) and -5.198kcal/mol (Figs. 1c and 1d), respectively. However, the most noticeable interfaces in the sequences were testified for ligand 47 with a binding kinship of -10.4kcal/mol. The binding affinity of the synthesized molecule showed a better score value than the reference drugs miconazole and ketoconazole. The molecule 47 efficiently fits into the catalytic pocket of the receptor (Fig. 1e). The phenyl ring attached at C2 of the imidazole moiety showed one carbon-hydrogen bond and one pi-pi interaction with the PIM 470 amino acid. It also showed π-cation, π- π stacked, π-alkyl, and π-sigma connections (Fig. 1f). The methyl group of the compound suitably interacted with the LYS 97 residue of protein to ensure proper fit.

(a)

(b)

(c)

Fig.1. The figure showed 2D interaction diagram of (a) Miconazole with 1E9X, (b) Ketoconazole with 1E9X, (c) best docked Compound 47 on 1E9X.

Table 3. Molecular docking result of some best docked compounds.

Compd. Code	Dock score	Compd. Code	Dock score	Compd. Code	Dock score
47.mol	-10.4	18.mol	-10.2	41.mol	-10.1
52.mol	-10.3	58.mol	-10.1	85.mol	-10.1
15.mol	-10.2	23.mol	-10.1	53.mol	-9.9
21.mol	-10.2	miconazole	-6.194	ketoconazole	-5.198

3.2. Generated Pharmacophore hypotheses:

Twenty theories (or hypotheses) were produced using various features. The derived hypotheses primarily provide structure-activity information when there are multiple chemical moieties present, but only a small number of them have comparable activities. The hypothesis basically satisfies five distinct variations or locations on which the appropriate alignment of ligands, or actives, is taken into consideration. Variants such as acceptor (A), donor (D), hydrophobic (H), positive ionic (P), and aromatic ring (R) are present in hypotheses. Based on how well active ligands fit the chosen hypothesis and how well they rank, the best hypotheses are chosen. AHRRR_2, HPRRR_3, HPRR_2, HHRRR_1, and PRRR_2 are the top 5 hypotheses because they provide the greatest amount of alignment that can be achieved between all active ligands and their corresponding variants or features. The pharmacophore hypothesis AHRRR_2 is accessible in Fig. 2(a–c). The topographies characterized by this theory are one hydrogen bond acceptor (A), one hydrophobic province (H), and three aromatic rings (R).

(a)

(b)

(c)

Fig. 2. The best pharmacophore hypothesis (a) AHRRR_2 (b) AHRRR_2 with active ligands (c) AHRRR_2 with inactive ligands.

Table 5. Statistical features of 3D QSAR model for PLS 1-7.

PLS Factor	SD	R^2	R^2 CV	R^2 Scramble	Stability	F	P	RMSE	Q^2	Pearson-r
1	0.4899	0.0892	0.0379	0.1436	0.433	1.8	0.0517	0.71	0.0299	0.3261
2	0.4883	0.1169	0.0063	0.2268	0.555	1.7	0.0831	0.74	-0.0515	0.2147
3	0.4893	0.1357	0.1091	0.2756	0.403	1.6	0.124	0.74	-0.0551	0.2894
4	0.489	0.2588	0.1504	0.3139	0.216	1.8	0.15	0.68	0.1041	0.5316
5	0.4912	0.2736	0.164	0.3378	0.281	2.0	0.197	0.66	0.171	0.6625
6	0.485	0.4162	0.3425	0.3593	0.427	2.6	0.161	0.61	0.2963	0.7139
7	0.4753	0.6682	0.5359	0.3827	0.847	4	0.112	0.58	0.3463	0.7497

3.3. 3D QSAR analysis:

The best statistical result for PLS 7 revealed 0.4753 (SD), 0.6682 (R^2), 0.5359 (R^2CV), 0.3827 (R^2 Scramble), 0.847 (Stability), 4 (F value), 0.112 (P value), 0.58 (RMSE), 0.3463 (Q^2), and 0.7497 (Pearson-r). Compound 23 showed an appropriate comparison between the projected or predicted activity (6.31) and the actual experimental activity (6.3639). With the aim of doing the regression, a number of mock-ups with increasing numbers of PLS components were created. The accuracy of the model surges with an increase in PLS factors until overfitting happens. The number of PLS factors that can be added is unlimited, but generally speaking, factor addition should stop when the regression's standard deviation is approximately equal to the experimental error. Statistical indicators such as stability (0.847) were close to 1, as were R^2, Pearson-r, and F values, which were also strong (0.6682, 0.7497, and 4, respectively) at the seventh PLS factor with the least regression SD (0.4753) (Table 4). Consequently, we selected the model generated with PLS 7 and random training set 85% to build our three-dimensional quantitative structure-activity model.

3.4. 3D QSAR visualization:

Envisaging the 3D QSAR prototypical in relation to one or more ligands in sequences through altered action can provide more understanding of the lanosterol 14α-demethylase inhibitory activity.

Gaussian steric feature prediction:

The Fig. 3a displays the 3D QSAR model centered on ligand 23 from the data set describing steric interaction. The green area surrounding the meta-fluoro of the phenyl ring substituted at position N1 and the p-tolyl ring substituted at position C2 of the imidazole scaffold suggests that groups with steric properties favor an increase in antagonistic activity when they substitute these rings. The impact of inhibitory activity on molecule 23 is comparatively weaker in the yellow regions.

Gaussian electrostatic feature prediction:

The Fig. 3b displays the 3D QSAR model utilizing the electrostatic feature based on molecule 23. The blue area surrounding N3, the phenyl assembly at C2, and the fourth carbon of the phenyl connected to the N1 slot of the imidazole scaffold all suggest that the antifungal activity is favorably affected by group substitutions at these locations by groups with stronger electrostatic properties. The red area surrounding the meta-fluoro group at the N1-substituted phenyl of the imidazole ring and the methyl clutch substituted in the para slot of the C2-attached phenyl showed that the electrostatic property does not favor the inhibitory action.

Gaussian hydrophobic feature prediction:

The Fig. 3c displays the 3D QSAR model constructed on molecule 23 that uses the hydrophobicity feature. The orange region surrounding the C2 imidazole ring and the meta-fluoro group substituted on the phenyl ring at position N1 indicates that the addition of hydrophobic groups will upsurge the antagonistic action, while the addition of hydrophobic groups at these positions will favor antifungal activity. The magenta region surrounding the methyl substituent of the phenyl ring at positions C2 and N2 on the imidazoline ring shows that groups with higher hydrophobicity are not more likely to engage in antagonistic activity.

(a). Gaussian steric feature (Green indicating positive and Yellow indicating negative side of activity)

(b). Gaussian electrostatic feature (Blue indicating positive and Red indicating negative side of activity)

(c). Gaussian hydrophobic feature (Orange indicating positive and Magenta indicating negative side of activity)

Fig. 3. Contours showing different Gaussian features for compound 23.

CONCLUSION:

Through docking experiments, we were able to demonstrate that the designed and synthesized imidazole derivatives efficiently interacted with antifungal proteins, i.e., human lanosterol 14α-demethylase (PDB: 1E9X). The drug-receptor interaction involves vander waals bond, hydrogen bond (carbon-hydrogen bond as well as conventional hydrogen bond), π-cation, π-sigma, π-alkyl, π-π stacked, π- π T-shaped, and carbon-alkyl interactions. The kind and location of the attachments on the aryl ring, as well as the slot of the aryl, have a significant impact on the alignment and interface of imidazole with the receptor, according to studies on molecular modeling. According to QSAR research, the substituent's steric, electrostatic, and hydrophobic features have a significant impact on the biological response. Indices such as stability (0.847), R^2 (0.6682), Pearson-r (0.7497), and F (4) values have the chief consequence on the antifungal activity of these types of azoles. The strong and specific inhibitory action of these substances can be well explained by observations and experimental findings. When it comes to executing the dynamic strategy or alteration of the class of 14α-demethylase inhibitors and comprehending the mechanism of their action, computationally oriented studies provide helpful allusions.

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Received on 30.03.2024      Revised on 22.06.2024

Accepted on 28.09.2024   Published on 02.05.2025

Available online from May 07, 2025

Research J. Pharmacy and Technology. 2025;18(5):2149-2154.

DOI: 10.52711/0974-360X.2025.00308

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Received on 30.03.2024      Revised on 22.06.2024

Accepted on 28.09.2024   Published on 02.05.2025

Available online from May 07, 2025

Research J. Pharmacy and Technology. 2025;18(5):2149-2154.

DOI: 10.52711/0974-360X.2025.00308

© RJPT All right reserved

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.