El fadili et al. BMC Chemistry(2024) 18:142
https://doi.org/10.1186/s13065-024-01248-6

BMC Chemistry

RESEARCH

Open AccessIn silico insights into the design of novel
NR2B-selective NMDA receptor antagonists:
QSAR modeling, ADME-toxicity predictions,molecular docking, and molecular dynamics
investigations
Mohamed El fadili1*, Mohammed Er-rajy1, Somdutt Mujwar2, Abduljelil Ajala3, Rachid Bouzammit4,
Mohammed Kara5, Hatem A. Abuelizz6, Sara Er-rahmani7 and Menana Elhallaoui1
Abstract
Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists,
two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques,
as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain,with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of
NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using
multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), andartificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole
moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest
unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885
and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation withcorrelation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation techniquewith the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability
domain (AD), in addition to Fisher’s and Student’s statistical tests. Thereafter, ten novel molecules were designed
based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined fortheir similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen formolecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex
with the targeted protein of NMDA receptor (5EWJ.pdb).
Keywords NMDA receptor, Analgesic activity, Molecular modeling, Neuropathic pain, MD*Correspondence:
Mohamed El fadilimohamed.elfadili@usmba.ac.ma
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
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El fadili et al. BMC Chemistry(2024) 18:142

Introduction
N-methyl-D-aspartate (NMDA) receptors are part ofionotropic receptors physiologically activated by glutamate and glycine, which play an essential role in learning,memory, and synaptic plasticity in the central nervous
system (CNS). They are mainly known for their permeability to divalent cations of calcium (Ca2+) and monovalent cations of potassium (K+) and sodium (Na+) in
membrane cells, due to their structural nature as a tetrameric combination of two GluN1subunits linked to glycine and two GluN2 subunits fixed to glutamate sites, inwhich the distribution of each NMDA subunit is closely
associated with neuronal plasticity and synaptic deficiencies [1]. Recently, it was discovered as a potential
therapeutic target for neurological and mental diseases,
including memory impairments, chronic pain, schizophrenia, Parkinson’s, and Alzheimer’s disorders [2–5]. In
this respect, the discovery of powerful NMDA receptor
antagonists is an absolute necessity for treating this classof neurodegenerative disorders, which requires a selective activation of each blocked receptor which occurs
more frequently for older cells than younger ones.
Nowadays, the computer-assisted drug design (CADD)
approach based on in silico techniques has taken on
enormous importance in the pharmaceutical sector,
aimed at discovering new drug candidates [6–8]. It has
been widely reported in the literature to save the timeand costs of experimentation before any pre-clinical andclinical tests. However, in vivo and in vitro tests remainstrongly recommended for each new compound designed
based on in silico studies [9–12]. Many scientific articles
were focused on NMDA receptors as a major neurotransmitter in the human brain, closely involved in the excitotoxicity process responsible for the pathophysiology
of various diseases, to concept the new competitive andnon-competitive NMDA receptor antagonists, which
could be recommended as an effective treatment for several neurological impairments such as Alzheimer’s, cerebral vascular accidents, and chronic pain [13–16].
The present work aims to concept novel NR2B-selective NMDAR antagonists using in-silico techniques
based on computer molecular modeling, including QSAR
modeling, ADME-Tox predictions, molecular docking,and molecular dynamics investigations.
In the first stage, QSAR technique was carried out toa structural class of thirty-two selective inhibitors of
NMDA receptor, acting as analgesic agents against neuropathic pain [5, 17], in which four QSAR models weregenerated using MLR, PLSR, MNLR, and ANN techniques to predict the linear and nonlinear relationshipbetween various molecular descriptors of NR2B-selective NMDAR antagonists and their analgesic activities of pIC50 order [18, 19]. The developed models wereapplied on a training set of twenty-six molecules andPage 2 of 22

then validated on a test set of six molecules [20]. Thereafter, ten novel molecules (C33 to C42) were designed
based on the most active compound (C22) and predicted
in humans by their absorption, distribution, metabolism,excretion, and toxicity (ADMET), then examined fortheir similarity to the drug candidates [21]. In the second stage, two novel-designed molecules labeled C37
and C39, were chosen for molecular docking to study
their inhibition mechanisms towards NMDA receptor,
encoded in protein data bank by 5EWJ.pdb [22]. In the
final stage, C37 and C39 molecules, which were predicted
to have greater analgesic activity than the most active
compound C22, were chosen for the molecular dynamics (MD) technique to test the thermodynamic stability of each (ligand-protein) complex [23, 24], which wasequally compared to the original compound (C22) over
100 nanoseconds of MD simulation time after being
complexed to the same protein target.

Materials and methods
Experimental database

A structural family of 32 NMDA receptor antagonists,
which were successfully examined using experimental
in vivo and in vitro assays to block the binding of [3 H]ifenprodil to rat brain membranes as authored by Kosuke
Anan, et al. [17], was chosen as an appropriate database
to concept new active molecules with the highest inhibitory activities using molecular modeling techniques,
where the analgesic activities against neuropathic pain of
the original molecules (C1 to C32) were expressed on a
decimal logarithm scale (pIC50 = - log10IC50), as presented
in Table 1.
Molecular descriptors calculation

To establish a reliable QSAR model, we have calculated
a variety of molecular descriptors as classified in Table 2,
where the thermodynamic and physicochemical descriptors were calculated by MM2 method using ChemBio3D
software [25], the constitutional descriptors were calculated by ACD/chemsketch software [26], at the sametime the geometry of the studied compounds was optimized using the density functional theory (DFT) with the
B3LYP functional [27], combined with the 6-31G + G(d,p) basis, thus the quantum descriptors were calculatedwith the help of Gaussian 09 package software [28].
Statistical methods

To generate the mathematical models that exhibit thequantitative relationships between the biological activity
and the calculated molecular descriptors, different statistical methods were used such as Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR),
Multiple Non-Linear Regression (MNLR) and Artificial
Neural Network (ANN).

El fadili et al. BMC Chemistry(2024) 18:142

Table 1 Studied molecules and their observed activities pIC50

Page 3 of 22

El fadili et al. BMC Chemistry(2024) 18:142

Table 2 List of different calculated descriptors
Type of descriptors
Quantum descriptors

Constitutional descriptors
Physicochemical descriptors
Thermodynamic descriptors

Name of descriptors
HOMO and LUMO Energies (Ehomo, E lumo) - Hardness (ƞ)
Dipole moment (µ) - Electronegativity (χ) - Gap Energy (E gap)
%C-%H-%O
Octanol-water partition coefficient (Log P) - Density (d)Kinetic Energy (K) - Potential
Energy (P) - Total Energy (T)As a first step, the number of molecular descriptors
was minimized using the Principal Component Analysis(PCA) by XLSTAT 2014 software, as a very important
step to make the studied information less redundant,
because it serves to reduce the size of the original variables that are correlated with each other into some synthetic variables that are independent from one another,called the principal components or factorial axes [29–31].
As a second step, the linear variation of these independent variables (uncorrelated descriptors) with the dependent variable (analgesic activity), was tested using MLR
method with stepwise selection. In this case, where there
are more predictors than observations and there is strong
collinearity between these predictors, the PLS method isthe best and it would be interesting to be able to study
more precisely the quality of the model by minimizing
the difference between the observed and calculated values [32]. Finally, the molecular descriptors used werechosen as input parameters in the multiple nonlinear
regression (MNLR) and artificial neural network (ANN).
All four models obtained were generated to predict theeffects of these NMDA antagonists on the rat brain. Eachtested model was treated according to the following statistical criteria: R, R2, and R2adj that must tend towards
1, which mean respectively: correlation coefficient, determination coefficient, and adjusted coefficient. Meansquare error (MSE) should be minimal (tend towards 0),
the Fisher value (F) must be inferior to the critical valueof Fisher, and the probability value (p-value), it’s better tobe under 5% for a 95% confidence level. To make thesefour QSAR models applicable, we have used the external
validation technique on six new compounds constituting
the test set. Also, we have performed the cross-validation
method with the “leave-one-out” procedure, in order toexamine the reliability of the developed models. So, thevalidity of the performance estimate will be obtained by
performing this conventional procedure, which is realized by removing a single example from the training base,applied each time on the N-1 compounds [33]. In addition to cross-validation method, two other techniques arevery important: the first one is “Y-randomization” technique; this sort of approaches is particularly appropriate
when we are not sure of our model of the data generation

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process [34]. To do this, we were based on our full set of
32 compounds, after that we have searched randomly a
new training test of 26 molecules to validate and ensure
the security of the statistical tests [35].
Drug likness and in-silico ADMET predictions

To examine the drug likness of novel chemical compound, the physicochemical feautures must satisfy atleast two of Lipinski’s five rules, with an acceptable
ADMET profile, and good oral biovailability by thehuman body, without any adverse toxic effects on thehuman body. For this purpose, SwissADMET, pKCSM,
and CLC Drug Discovery servers were carefully used totest the physicochemical and pharmacokinetic profiles[36, 37] of nine chemical compounds designed based on
QSAR results.
Molecular docking modeling

Molecular docking is often used in computational chemistry to accelerate drug discovery at early stages [9]. For
this project, this molecular modeling technique is basedon the cell key phenomenon, where the best position of
the ligand or drug candidate (the agent) is the key thatcan open the cell (or protein) to have finally a more stablecomplex by energetic order [38].
The crystal structure of amino-terminal domains of the
NMDA receptor subunit GluN1 and GluN2B in complex
with ifenprodil, coded in protein data bank by 5EWJ.
pdb (https://www.rcsb.org/structure/5EWJ) was chosen as the targeted protein of NMDA receptor, in which
can complex the inhibitors studied [39]. The responsible
protein was discovered using X-ray diffraction method,in machine simulation with a resolution equal to 2.77 Å[40]. Then, it was prepared by removing all the water molecules bound, the sodium atom, and all the suspended
ligands were removed, while adding the polar hydrogens
using the Discovery Studio software, for the reason thatthe cavity method works best [41]. All this is to indicate
the map coordinates of the co-crystallized ligand. Afterthe preparation of the protein and quoting its active site,
we have launched the docking calculation in AutoDock
4.2 program. In this way we have docked in the protein
receptor three molecules already optimized by the DFT
theory (output file): the first one is noted C22, having thegreatest analgesic activity against neuropathic disease of
pIC50 = 7.9, and the second ones are the new predicted
molecules (C37 and C39) to compare their mechanisms
of inhibition towards the most active compound. With
the help of algorithm AUTOGRID We were able to centralize the grid boxes on (86.63 Å, -7.51 Å, -64.01 Å) byputting the sizes: 110, 110, and 110 in their three-dimensional structure, and running 10 genetic algorithms, witha total of two million five hundred thousand evals. At
the end, we have got the strongest complex out of fiftyEl fadili et al. BMC Chemistry(2024) 18:142

conformations obtained [42], and we have visualized the2D and 3D interactions of the protein-ligand by discovery
studio 2020 [43].
Molecular dynamics simulation

To validate the thermodynamic stability of ligands C37,
C39, and standard drug C22 with the NMDA receptor
for their analgesic effect were shortlisted for performing
MD simulation based upon their docking score, physicochemical analysis and observed chemical interactions
with the target receptors. All the above said MD simulations were executed for a time period of 100 ns by using
Desmond module of Schrodinger’s Maestro software[44]. Addition of explicit solvent molecules followed bytheir neutralization by adding the respective ions. The
steepest-descent algorithm was used to relax the systemand eliminate any steric clashes or poor contacts within
atoms in order to minimize the system’s energy. Using ashort series having low temperature with constant pressure (NPT) simulations, the system was brought to equilibrium. Positional constraints are applied to the system
in addition to a progressive increase in temperature [45–47]. This makes it more likely that the system will be ina stable, balanced state prior to the simulation, in orderto get the appropriate outcomes, the simulation is performed for 100 ns while taking into account the system’senergies, atom positions, and RMSD values. This aids incomprehending the system’s dynamic behavior and provides long-term intuitions on the complex’s structure andfunctional stability [24, 48–50].

Results and discussions
Statistical database

To establish the QSAR mathematical models, we haveselected a set of 32 compounds from recently published work of K.Anan, et al., including NR2B-selective

Page 5 of 22

antagonists that Inhibit binding of [3 H]-ifenprodil to rat
brain membranes [17]. Therefore, the database will be
represented as a matrix of 32 rows (active molecules) and
14 columns (molecular descriptors) as illustrated in Table
S1. Then, we worked randomly to divide this complete set
into two subsets: the first one (training set) includes 26
compounds that are used to build the model and the second one (test set) includes 6 compounds to validate thisestablished model.
Principal component analysis

To reduce the size of the basic data, we have applied theprincipal component analysis method, calculating a number of linear combinations of the original variables such a
way to summarize the data with minimal loss of information, focusing on the correlation matrix given in Table S2[51].
According to the correlation matrix, the variables thathave a correlation coefficient absolutely higher than 90%
are strongly correlated. So, we have found 9 following
correlations:First, the density is strongly and negatively correlatedwith % C and % H of -94.5% and − 96.1% respectively,
also a strong positive correlation of 94.7% between % C
and % H, this means that the three are correlated witheach other several times. Second, it was observed that:the potential energy (P), kinetic energy (K) and the totalenergy (T), are totally correlated because the correlation
coefficient is equal to 1 in absolute value. Third, E Homo,
E gap and hardness are strongly correlated with eachother, such as: r(E homo, E gap)= -90.2%, r(E homo, ƞ)=-90.4% and r(ƞ, E gap) = 99.8%. Finally, we have dropped
these nine descriptors because every three variables
share the same information, as they are largely correlatedwith each other. Thus, we have limited our study to five
major uncorrelated descriptors pictured in Fig. 1.
These descriptors are projected on the first two principal components t1 and t2. Since, 46.074% of the studied
variables are explained by the first principal component,
24.901% of the variables contribute to the constructionof the second and only 11.441% of the variables contribute to the construction of the third principal component.
Therefore, we can propose that only the first two principal components are sufficient to obtain a good explanation of the data, because the variability explained by the
first two principal components is: 70.97% of the total
variability, as illustrated in Fig. 2.
Multiple linear regression

Fig. 1 The projection of the uncorrelated variables on the plane of thefirst two principal components t1 and t2

The Multiple Linear Regression is a statistical techniquefor estimating the relationship between more than two
variables which have cause-effect relations [52]. For
that, we have applied this technique on the training test
including twenty-six observations (N = 26) and we haveEl fadili et al. BMC Chemistry(2024) 18:142

Page 6 of 22

Table 4 Analysis of variance (ANOVA 1)
SourceModel
Error
Adjusted total

Fig. 2 The explanatory power of the principal components

Table 3 Significance test of the slopes
SourceValuetPr > |t|Lower
Terminal(95%)

Constant
E lumo
X
µ
Log P
%O

-5.03142.02865.706-0.2281.308
0.244-2.341
2.174
3.567-3.353
9.038
5.974

0.030
0.042
0.002
0.003
< 0.0001< 0.0001-9.5141.706
27.277-0.371
1.006
0.159

HigherTerminal(95%)-0.54982.349104.134-0.086
1.610
0.329

DDL
5
20
25

Total square Mean square F
Pr > F
9.103
1.821
24.664 < 0.0001
1.476
0.074
10.579in Fig. 3, because each increase in activity is accompanied by an increase of 42.028 kcal/mol in the energy ofthe lowest unoccupied molecular orbital, 65.706 in theelectronegativity of the compound, 1.308 log P (Hight
lipophilicity), and 0.244% of oxygen, but results in adecrease of 0.228 in the dipole moment. For a 95% confidence interval, the null hypothesis (H0) posed by the
Fisher statistical test is rejected, because the calculated
Fisher value (F = 24.664) is much higher than the criticalvalue: [F (26, 5) = 2.59, p < 0.0001], as simplified by theone ANOVA test (Table 4). So, the variance is homogeneous between the dependent variable (pIC50) and thefive explicative variables. In addition, the determination,correlation, and adjustment coefficients of R = 0.928,
R²=0.860 and R²adjusted = 0.826, show a strong relationship between descriptors and answers. So, the first QSAR
model obtained by MLR has good predictive competence, with a minimal standard error (RMSE = 0.272).
Partial least squares regression (PLSR)

To model a linear relationship between a set of predictors
and a response variable, so that the variance is minimal,
we have applied the partial least squares regression technique, in a way that solves linear regression problems andto have finally a good quality of adjustment, and a goodpredictive power [53, 54]. The results of this regression
technique yield the second QSAR model given in the following Eq. 2:
pIC50 = −3.696 − 20.573 ∗ E lumo + 37.531 ∗ X + 2.353E-02 ∗ µ(2)+ 1.229 ∗ Log P + 0.121 ∗ %O.

Fig. 3 Degree of influence of the descriptors on the activity

found the best following QSAR model as illustrated in
the following equation:
pIC50 = −5.031 + 42.028 ∗ E lumo + 65.706 ∗ χ − 0.228 ∗ µ(1)+ 1.308 ∗ Log P + 0.244 ∗ %O

This established model shows that biological activity,
is a quantitative variable significantly influenced by thefollowing five descriptors: E lumo, χ, µ, Log P and % O,
because the probability corresponding to the slope ofeach variable is less than 5% as noted in Table 3.
To know, all the variables have a positive weight on the
biological activity except the dipole moment, as shown

This model generated by the PLSR technique, is also
applied on the same training set of twenty-six observations (N = 26) and given by the following statistical criteria: the standard error (RMSE = 0.314) is minimal, thedetermination and correlation coefficients which arerespectively: R = 0.870 and R²=0.758, show that there is agood quality of adjustment and a good predictive powerof the model made by the regression on the partial leastsquares.
Multiple non-linear regression (MNLR)

Non-linear regression analysis is a type of regression
analysis in which the data are modeled by a non-linear
combination of several independent variables [55]. To
realize this technique, we have used preprogrammed
function of type:El fadili et al. BMC Chemistry

Y = a0 +(2024) 18:142

n 
i=1


ai ∗ Xi + bi ∗ Xi2 (3)As: Y: the predicted biological activity (pIC50), Xi: theexplicative variable, a0: the constant of the QSAR model,
a.i.: the slope of each descriptor to one degree and bi:
the slope of each descriptor of two degrees. This function presents the non-linear combination of the biological activity (pIC50) as a function of the five independent
variables found previously by the linear model QSAR,
based on the same training set of twenty-six observations(N = 26). So, the final results produce the third QSAR
model as indicated in the following Eq. 4:
pIC50 = 23.360 + 64.956 ∗ E lumo − 354.817 ∗ X − 0.360 ∗ µ+ 3.680 ∗ log P − 0.166 ∗ % O + 297.460 ∗ E lumo2
(4)+ 1599.438 ∗ X2 + 2.258E-2 ∗ µ2
− 0.550 ∗ log P2 + 1.461E-2 ∗ %O2

This mathematical model is defined by a good coefficientof determination (R = 0.941) and a good correlation coefficient (R2 = 0.885), in addition the model Root Mean
Square Error is minimal (RMSE = 0.285), this means thatthe last QSAR model generated by the MNLR technique
has a good predictive competence and a strong non-linear relationship between descriptors and answer.
Artificial neural network (ANN)One of the most important steps in QSAR studies is todevelop a non-linear relationship between activity andmolecular descriptors, using the ANN method [56],which differs from the function of biological neurons ina number of parameters such as structures, layers, computational style, processing speed, connections, strength,
storage and transmission of information [57]. In the present study, we have used three-layer neural networks, theinput layer contains 5 neurons representing the selected
descriptors, the hidden layer contains 3 neurons (H1, H2and H3), and the output layer represents the observed

Fig. 4 Schematic diagram of three-layer artificial neural network

Page 7 of 22

activity values (pIC50). A parameter ρ serves a majorrole in the determination of the best ANN architecture,
is used to identify the number of hidden neurons [58],which must be in the interval: 1 < ρ < 3 [59]. It’s given by
the following Eq. 5:

ρ=The number of weight(5)The number of connections in the NN

So, the architecture of the Artificial Neural Networks
used in this work is [5–3–1], as shown in Fig. 4.
The obtained results of the artificial neural network,
using the JMP version 8.0 software, show a very goodcorrelation of 97.7% between the observed and predictedvalues, with a minimal coded residual standard error
equal to 0.189 and an external validation correlation coefficient of 98.1%, operating on 16 turns and 2.734 of overadjustment penalty, with 75 maximum iterations, and
0.00001 of convergence criteria. This indicates that thedescriptors selected by multiple linear regression, are relevant, and the model has a high statistical quality.
Validation techniques
External validation

To evaluate the accuracy of four predictive models andensure their generalizability, it is essential to validatethem externally before they are applied in clinical practice [60]. For this purpose, we have tested the six newmolecules constituting the test set on a training test basis
and we have arrived at the results presented in Table 5.
Based on the set test (N = 6), the first regression established by MLR method and illustrated in Fig. 5, results anexternal validation correlation coefficient (R2ext = 0.703)
between the observed and predicted activities, withan adjustment coefficient (R2adjusted = 0.629) and aminimal root mean square error (RMSE = 0.460). The
second regression shown in Fig. 6, gives an external validation correlation coefficient (R2ext = 0.851) between theEl fadili et al. BMC Chemistry(2024) 18:142

Page 8 of 22

Table 5 The results of external validation by the MLR, PLSR and
MNLR methods
N°
C4*
C19*
C24*
C26*
C28*
C30*

Observed pIC50
7.200
7.700
7.800
6.000
6.200
7.200

pIC50 MLR
6.644
6.975
6.526
7.375
7.470
7.094

pIC50 PLSR
7.021
6.864
6.565
7.481
7.371
7.211

pIC50 MNLR
6.749
6.914
6.721
7.334
7.396
7.113

*Indicates the test set molecules

observed and predicted activities, with a good adjustment coefficient (R2adjusted = 0.814) and a minimal root
mean square error (RMSE = 0.326). The third regression
as presented in Fig. 7, gives an external validation correlation coefficient (R2ext = 0.778) between the observed andpredicted activities, with a good adjustment coefficient(R2adjusted = 0.722) and a minimal root mean squareerror (RMSE = 0.398) and the last regression, results
an external validation correlation coefficient of 98.1%.
According to ‘Alexander Golbraikh and Alexander Tropsha’ theory, an external validation correlation coefficient
greater than 0.6 shows that the established QSAR modelis externally validated. Consequently, we can say that thefour QSAR models obtained respectively by MLR, PLSR,
and MNLR techniques are externally validated because
their correlation coefficients are largely higher than 0.6,
so the experimental activity can be precisely predictedusing one of four established QSAR models including all
five of the following descriptors: E lumo, χ, µ, Log P and% [61].
Cross-validation

Fig. 5 Correlation between the observed and predicted activities using
MLR technique

To measure the effectiveness of the QSAR models andexamine their reliability, we have applied the crossvalidation technique with the leave-one-out procedure,
removing each observation at each iteration, so thateach observation is tested exactly a once [62, 63]. This
technique is applied searching each time a new model oftwenty-five compounds (N-1 = 25) and predicting the biological activity of the deleted sample, as noted in Table 6,
such that quadratic coefficient q2 or (r2cv) given by Eq. 6
is greater than 0.5, for that the models will be internallyvalidated [64].

 26(Y pred − Y obs) 2
r 2cv = 1 −  261
= 0.785 (6)2
1 (Y obs − Y mean)
Fig. 6 Correlation between the observed and predicted activities using
PLSR techniqueFig. 7 Correlation between the observed and predicted activities using
MNLR techniqueAs:
Y pred: the LOO predicted response value, Y obs: theobserved response value and Y mean: is the average ofthe observed response values.
A high value of r2cv = 0.785 (greater than 0.5) indicatesthat the candidate QSAR model is reliable, robust, andhas better internal predictivity. Despite this, ‘Alexander
Golbraikh and Alexander Tropsha’s study confirms thatthe cross-validation technique is essential but not sufficient, because the internal predictive power of the crossvalidation procedure tends to be overestimated and thehigh value of the quadratic coefficient may result froma hazard correlation. For this reason, a Y-randomization
test is necessary [61].

El fadili et al. BMC Chemistry(2024) 18:142

Page 9 of 22

Table 6 Observed and predicted pIC50 values of the training set
from the QSAR models
Compounds
NumberObserved
pIC50

Pred
pIC50
MLR

Pred
pIC50
PLSR

Pred
pIC50
MNLR

C1
C2
C3
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C20
C21
C22
C23
C25
C27
C29
C31
C32

7.100
7.100
7.200
7.300
7.600
7.500
6.000
6.000
7.500
6.300
7.800
6.000
6.000
6.000
6.900
7.600
6.600
6.800
7.500
7.900
7.800
7.500
7.200
7.300
7.900
7.300

7.032
7.007
7.229
6.983
7.450
7.303
5.835
6.176
7.569
6.495
7.253
6.079
6.174
6.409
6.715
7.810
6.327
7.129
7.468
7.508
7.874
7.390
7.641
7.364
7.924
7.555

6.652
6.809
7.386
7.166
7.596
7.540
6.267
6.491
7.970
6.645
7.125
5.910
6.259
6.443
6.620
7.631
6.339
6.994
7.363
7.373
7.831
7.255
7.490
7.170
7.684
7.691

7.113
7.089
7.172
7.147
7.561
7.390
5.804
6.229
7.434
6.395
7.239
5.911
6.200
6.279
6.584
7.843
6.621
7.136
7.485
7.539
7.898
7.336
7.591
7.338
7.867
7.496

Pred
pIC50
CV
(LOO)
6.985
6.963
7.246
6.933
7.419
7.240
5.654
6.262
7.623
6.534
7.209
6.108
6.333
6.504
6.690
7.844
6.129
7.190
7.466
7.469
7.888
7.347
7.687
7.370
7.929
7.585

Validation using the Y-randomization test

To control the robustness of the QSAR model, we haveused the Y-randomization test (or permutation test),
based on repetitive randomizations of the answer data
(Y). Thus, a new model is derived on a new training testof 26 compounds. But to improve the precision of theprobability level, a few hundred runs of rerandomized
data are usually necessary [65, 66]. The results of the validation using Y-randomization test displayed in Table S3,give the following statistical regression data of random
model’s parameters:

Average r : 0.421739, Average r2 = 0.192447,
Average Q2cv = −0.42364, and cR2 p = 0.766375.
Although the re-ordered data give much lower R2 thanthe original data, and randomization constant (cR2p) is
superior than 0.5, so we can be sure that the previous
MLR QSAR model is correct, robust, and is not due torandom chance.

Applicability domain

The main objective of the applicability domain (AD) for a
predictive classification QSAR model is to define the areaof chemical space where the model makes predictions
with a given reliability [67]. For this goal, AD was carriedout for the best QSAR model established by the MLR
technique as given in Eq. 1, based on the leverage analysis
illustrated by William diagram (Fig. 8) [68], which is performed with the assistance of SPSS software. An observation with hi > h* significantly affects the performanceof the regression and can be classified as an outlier not
associated to a reliable prediction. To know, the warningleverage (h*) is defined as h*=3*K/n, and K = p + 1, Wheren: is the number of the training set, and p: is the numberof predictor descriptors [69].
The results show that the leverage values of all chemicalcompounds in the training and test sets, more than ten
newly designed compounds were inferior to the warning leverage h* = 0.69 (p = 5, K = 6, n = 26) without exception. So, the QSAR MLR model was predicted correctly
due to the absence of outliers. Therefore, all compounds
are tested in the AD, which mentions that their predictedactivities are reliable [70].
Drug similarity prediction of C22 compound, and novel
molecules based on Lipinski, Ghose, Muegge, Veber andEgan rules

To arrive at a drug-like molecule, at least two of thefollowing Lipinski’s rules must be verified: Molecularweight (≤ 500 g/mol) - Log P (High lipophilicity < 5) Hydrogen bond donors (< 5) - Hydrogen bond acceptors(≤ 10) − 40 ≤ Molar refractive index ≤ 130. The number ofrotatable bonds must be less than 10 [71, 72]. In addition, Lipinski’s violations must not exceed 1, otherwise
the molecule may have bioavailability problems and ahigh probability of not being a drug [73]. The molecular
descriptors of new designed molecules (C33 to C42) as
presented in the Table 7, were calculated using Density
Functional Theory (DFT) technique, and then tested
with their synthetic accessibility and their drug similarity rules, specifically Lipinski, Ghose, Egan, Veber andMuegge, based on the most active compound, scored
C22.
The results obtained in Table 8, demonstrate that all ten
compounds respect all Lipinski rules, which indicates theabsence of oral bioavailability problems. All compounds
are easy to synthesize, as their synthetic accessibility values are all about 3. In addition, all designed compounds
meet the drug similarity rules except C36, C37 and C40
that do not verify Ghose and Egan drug similarity rules,
as presented in Table 9 [74].

El fadili et al. BMC Chemistry(2024) 18:142

Page 10 of 22

Fig. 8 William’s diagram of MLR model established by Eq: (1)

ADMET in silico pharmacokinetics

To identify the new candidate drug with high successlevel and reduced experimental study duration, newcompounds were designed based on the most potent
inhibitor scored (C22), as presented in Table 7. This insilico study predicts the pharmacokinetic features of
Adsorption, Distribution, Metabolism, Excretion and
Toxicity (ADMET) [75] for ten compounds satisfying all
conditions mentioned in Lipinski’s rule, as illustrated inTable 8. According to the results presented in Table 10,
all the proposed compounds have a good absorption in
the human intestine (IAH more than 70%) [72]. Except
for C36, C37, C38 and C39, all the new compounds havea good distribution, as their human distribution volumes
are estimated to be higher than − 0.44 Log L/kg, their
BBB permeability superior than − 1 Log BB, and their
CNS permeability included between − 2 and − 3 Log PS,
thus do not penetrate the central nervous system [76].
C36 and C37 compounds, are potential inhibitors ofcytochrome CYP450 (2C19), C33 to C39 molecules are
possible inhibitors of cytochrome CYP450 (3A4), andC33-34 and C36-39 molecules could be inhibitors ofcytochrome CYP450 (2C9). These last inhibitors are theonly ones that have a low total clearance value, whichmeans a great success of drug elimination by the organism. Any of these molecules having the AMES toxicity,
this last property of ADMET has a major role in drug
discovery [73]. Finally, we have succeeded in finding two
new molecules, with highest analgesic activities (morethan most active compound C22); verifying Lipinski,

Muegge, Veber and Egan rules, and all in silico ADMET
properties without exception, the first one noted C37 andthe second one noted C39, they are predicted as powerful inhibitors of both cytochromes CYP450 (2C9) andCYP450 (3A4), with analgesic activities of pIC50 = 8.526
and pIC50 = 7.991, respectively.
Additionally, the bioavailability test confirm that allthese novel compounds were predicted with an excellent oral bioavailability by the human body, because alltested molecules are part of the pink area of bioavailability radars presented in Fig. 9, as a desired part selected on
the basis of the physicochemical parameters of flexibility,
saturation, solubility, lipophilicity, polarity and size.
Molecular docking

The results of molecular docking simulations shown in
Fig. 10, confirm that both novel designed molecules (C37
and C39) were efffectively docked to the active sites of
NMDA receptor with lowest possible binding energies
in kcal/mol (-8.22 and − 8.14, respectively.), producing
common intermolecular interactions, like those detected
towards Gln110, Tyr109 amino acids residues (AARs),
which were the same chemical bounds detected with themost active compound labeled C22, which was equally
docked to the same targeted receptor with a binding
energy closer than the previous ones (-7.84 kcal/mol).
All other types of intermolecular interactions resulted ineach (ligand-5EWJ.pdb protein) complex are presented
in Table 11, in which we have equally noticed that bothdesigned molecules (C37 and C39) in addition to theEl fadili et al. BMC Chemistry(2024) 18:142

Page 11 of 22

Table 7 Structural formula of the new studied compounds

Table 8 Lipinski rules of the newly designed compounds
Compounds
Rule
C33
C34
C35
C36
C37
C38
C39
C40
C41
C42

Physicochemical properties
Molecular weight (g/mol)≤ 500
418.81435.26426.42499.73499.73450.37450.37398.37382.37382.37

Lipinski violations
Molar refractive index
40 ≤ MR ≤ 130
112.10117.15120.11
128.39128.39114.15114.15111.17109.15109.15

Log P

H-BA

H-BD

<5
2.28
1.88
2.61
2.51
2.43
2.13
2.02
1.71
1.96
1.74

≤ 10
5
4
6
5
5
8
8
6
5
5

<5
2
2
2
3
3
3
3
4
3
3

≤1
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

El fadili et al. BMC Chemistry(2024) 18:142

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Table 9 Drug likeness prediction of the selected compounds and C22 compound based on Ghose, Muegge, Veber, and Egan rules,
and their synthetic accessibility
Compounds
C33
C34
C35
C36
C37
C38
C39
C40
C41
C42

Muegge
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Veber
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

most active ligand (C22) were docked no further thanprotein target active sites, including Ser132 AAR in A
chain and Gln110 AAR in B chain (Fig. 11), which affirm
again that the studied ligands were actually docked tothe active sites of the protein target, so the processes ofmolecular docking are successfully validated.
MD simulation

Drug-receptor complex has to be sufficiently stable
over a nano-scaler time range to execute the therapeutic response. As a result, the macromolecular complex
underwent a 100 ns MD simulation by using Schrodinger’s Desmond software version 2022.4. The NMDA
receptor’s dimeric structure is having of 710 amino acids
consisting of 5502 heavy atoms out of overall 10,994
atoms. Structural alterations and RMSD analysis of themacromolecular backbone was executed during the 100
ns simulation to evaluate their thermodynamic stability. The complexed ligand C37 comprising of thirty-two
heavy atoms of fifty-eight atoms in total with the presence of seven rotatable bonds. The RMSD value of the
NMDA receptor’s backbone was found to fluctuate
between 1.5 and 4.0 Å, whereas the bound ligand C37
exhibited some fluctuations within the macromolecular
cavity with RMSD value in range of 3.0-5.5 Å followed bya sharp conformational change within the target cavity.
The atoms in a protein or ligand structure might deviate from their initial location, and this can be measured
by using their RMSF value. It is an important parameterfor determining the flexibility and dynamic behavior of
the macromolecular complex. Protein RMSF is important because it may be used to predict protein dynamics
and evaluate stability by providing information about therelative flexibility of various regions. MD based evaluation of human NMDA receptor complexed with C37 hasconcluded that the RMSF for Cα backbone was found to
be within 0.6–3.2 Å with couple of exceptions, while forligand C37 higher RMSF value lies withing the range of
2–4 Å within the target cavity.

Egan
Yes
Yes
Yes
Yes
Yes
Yes
Yes
NO
Yes
Yes

Ghose
Yes
Yes
Yes
NO
NO
Yes
Yes
Yes
Yes
Yes

Synthetic accessibility
3.23
3.26
3.50
3.33
3.33
3.26
3.28
3.30
3.25
3.21

The development of hydrophobic contacts, ionic interactions, and hydrogen bonds during an MD simulation
are responsible for the thermodynamic permanence of a
receptor-ligand complex and it is evaluated by the continuous monitoring of their strengths throughout thesimulation for all the three macromolecular complexes.
Throughout the simulation ligand C37 was found to beinteracting with the NMDA receptor via formation ofhydrophobic bonds with the amino acids Tyr109_A,
Ile127_A, Tyr128_A, Ile133_A, His134_A, Met134_B,
and Pro177_B, whereas amino acid Tyr109_A, Ser132_A,
Leu135_A, Lys137_B, and Asp138_B via hydrogen bonding, while residues Ser108_A, Tyr109_A, Ser132_A,
Ser136_A, Gln110_B, Asp113_B, Ala135_B, Asp136_B
and Asp138_B are found to be interacting via water
bridges.
The complexed ligand M7 comprising of thirty-two
heavy atoms of forty-nine atoms in total with the presence of seven rotatable bonds. The RMSD value of thereceptor’s backbone was found to fluctuate between 1.5
and 3.5 Å, whereas the bound ligand C39 exhibited stableconformation throughout the simulation within the macromolecular cavity with RMSD value in range of 6.0–9.0
Å within the target cavity.
MD based evaluation of NMDA receptor complexedwith ligand C39 has concluded that the RMSF for Cα
backbone was found to be within 0.8–2.4 Å with coupleof exceptions, while for ligand C39 is having RMSF value
within the range of 2.0–3.0 Å. indicating its high stability within the target cavity. Throughout the simulation
ligand C39 was found to be interacting with NMDA
receptor via formation of hydrophobic bonds with theamino acids Tyr109_A, Phe113_A, Ile127_A, Ile133_A,
His134_A, and Ala107_B, whereas residues Thr105_A,
Gly112_A, Ser129_A, Asp130_A, Gln105_B, Gln110_B,
and Asp136_B, Val43, Ala47, Val71, Ile78, Ile90, Val120,
and Val167 are found to be interacting via hydrogen bonding, while amino acid Thr105_A, Ser108_A,
Arg115_A, Asp130-A, Ser132_A, Ser136_A, Thr76_B,
Glu106_B, Asp113_B, Asp136_B and Asp138_BAsn46,

C33
C34
C35
C36
C37
C38
C39
C40
C41
C42

Compounds

Numeric(Log L/kg)-0.376-0.244-0.196-0.455-0.447-0.535-0.529-0.025-0.051-0.056

Numeric (%Absorbed)
84.96585.861
78.69
79.35579.41879.52679.58871.14777.50377.44-0.91-0.872-0.917-1.123-1.123-1.006-1.006-0.904-0.709-0.709

Numeric (Log BB)

Distribution
VDss
BBB permeability(human)Absorption
Intestinal absorption(human)-2.442-2.286-2.882-2.342-2.344-2.544-2.547-2.896-2.692-2.694

Numeric (Log PS)

CNS permeability

Table 10 ADMET properties prediction of newly engineered compounds
Inhibitor

No
No
No
No
No
No
No
No
No
No

Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No

No
No
No
No
No
No
No
No
No
No

No
No
No
Yes
Yes
No
No
No
No
No

CYP
2D6 3A4 1A2 2C19
Categorical (Yes/No)

Substrate

Metabolism

Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No

2C9

No
No
No
No
No
No
No
No
No
No

2D6

Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No

3A4
Numeric(Log ml/min/kg)-0.0410.045
0.667-0.184-0.125
0.389
0.414
0.553
0.765
0.575

Excretion
Total
Clearance

Not toxic
Not toxic
Not toxic
Not toxic
Not toxic
Not toxic
Not toxic
Not toxic
Not toxic
Not toxic

Categorical (Yes/No)

Toxicity
AMES toxicity

El fadili et al. BMC Chemistry(2024) 18:142
Page 13 of 22

El fadili et al. BMC Chemistry(2024) 18:142

Fig. 9 Bioavailability radars for ten designed molecules (C33 to C42)

Page 14 of 22

El fadili et al. BMC Chemistry(2024) 18:142

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Fig. 10 2D and 3D docking positions illustrating the resulted interactions for C37, C39, and C22 phenylpiperazine derivatives, in complex with NMDA
receptor coded as 5EWJ.pdb, with binding energies of -8.22, -8.14, and − 7.84 kcal/mol, respectively

Asp49, Glu50, Asp73, Arg76, and Gly77 are found to beinteracting via water bridges.
The complexed standard drug C22 comprising oftwenty-nine heavy atoms of forty-eight atoms in total
with the presence of five rotatable bonds. The RMSD
value of the NMDA receptor’s backbone was found tofluctuate between 1.5 and 2.7 Å, whereas the bound ligand
C22 exhibited stable conformation throughout the simulation within the macromolecular cavity with RMSD value
in range of 4.0-6.4 Å within the target cavity. Figure 12
demonstrates the revealed RMSD for macromolecularEl fadili et al. BMC Chemistry(2024) 18:142

Page 16 of 22

Table 11 Types of intermolecular interactions produced in three studied complexes

Fig. 11 Active sites in A and B chains of NMDA receptor coded by 5EWJ.pdb code

complexes of NMDA receptor complexed with ligand (a)C37, (b) C39, and standard drug (c) C22.
MD based evaluation of human NMDA receptor complexed with ligand C22 has concluded that the RMSF forCα backbone was found to be within 0.6–2.4 Å with couple of exceptions, while for ligand C22 is having RMSF
value within the range of 2.0–3.0 Å. indicating its highstability within the target cavity. RMSF for macromolecular complexes of human NMDA receptor complexed withligand (a) C37, (b) C39, and standard drug (c) C22 wasdepicted in Fig. 13.
Throughout the simulation ligand C22 was found to beinteracting with human NMDA receptor via formationof hydrophobic bonds with the amino acids Ile133_A,

El fadili et al. BMC Chemistry(2024) 18:142

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Fig. 12 RMSD for Cα chain backbone and complexed ligand for the macromolecular complex of NMDA receptor with ligands C37, C39, and standard
drug C22 respectively, detected while executing 100ns MD simulation

His171_A, Met134_B, Phe176_B and Pro177_B, whereas
residues Gln110_B, Asp136_B, and Asp138_B are foundto be interacting via hydrogen bonding, while amino acidIle127_A, Ser132_A, His134_A, Glu106_B, Gln110_B,

Met132_B, Ile133_B, Asp136_B, and Asp138_B are foundto be interacting via water bridges. Figure 14 illustrates
the interacting residues of human NMDA receptor withcomplexed ligand C37, C39, and standard drug C22.

El fadili et al. BMC Chemistry(2024) 18:142

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Fig. 13 Root mean square fluctuation: Observed RMSF for the macromolecular complex of C37, C39, and standard drug C22 respectively, with human
NMDA receptor detected while executing 100ns MD simulation

Conclusions
To concept new analgesic drugs for the treatment of neuropathic pain, four QSAR models were developed andsuccessfully validated using different assessment methods, in which the analgesic activity was significantly
affected by the physicochemical, constitutional, andquantum descriptors, in particular: dipole moment, octanol/water partition coefficient, Oxygen mass percentage,electronegativity, and energy of the lowest unoccupied
molecular orbital. These results are partially qualified
by molecular docking study, as the intermolecular interactions produced by the most active compound (C22)
towards the active sites of protein target coded 5EWJ,
confirm the production of the chemical bonds withhighly electronegativity atoms, such as fluorine and chlorine in meta and para positions, respectively. This is ingood agreement with the results developed by the QSAR
model, as a noticeable increase in electronegativity, inthe presence of an enriched mass percentage of the oxygen element renders the ligand a highly potent analgesic.
These results could provide important structural information needed to optimize new good drug candidates forthe treatment of neuropathic disease. Among these new
drugs, two non-toxic compounds noted C37 and C39,
respectively, were predicted to satisfy Lipinski, Muegge,
Veber and Egan rules, with excellent ADMET profiles, and very good level of molecular stability towards
NMDA receptor. For these reasons, C37 and C39 arestrongly recommended to treat neuropathic pain. However, they must be subjected to experimental in vivo andin vitro investigations to examine their safety and efficacy
as anti-chronic pain analgesic.

El fadili et al. BMC Chemistry(2024) 18:142

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Fig. 14 Protein-ligand contacts: Protein-ligand interactions identified between human NMDA receptor complexed with ligands C37, C39, and standard
drug C22. The interactions were visualized using different colored bars, with green representing hydrogen bonds, blue representing water bridges, andpurple representing hydrophobic interactions

El fadili et al. BMC Chemistry(2024) 18:142

Supplementary Information

Page 20 of 22

3.

The online version contains supplementary material available at https://doi.
org/10.1186/s13065-024-01248-6.
Supplementary Material 1
Acknowledgements
The authors extend their appreciation to the Researchers Supporting Project,
King Saud University, Riyadh, Saudi Arabia for funding this work through grant
no. RSPD2024R566.
Author contributions
Author Contributions: M.EF: wrote the main manuscript text, M.ER and S.M:
software, A.A and R.B, H.A: reviewed the manuscript, M.K and S.E: analysis andvalidation, M.E; Supervised and reviewed the experiments.
Funding
This research was funded by the Researchers Supporting Project (No.
RSPD2024R566), King Saud University, Riyadh, Saudi Arabia.
Data availability
No datasets were generated or analysed during the current study.

4.

5.

6.

7.

Declarations
Competing interests
The authors declare no competing interests.

8.

Institutional review board statement
Not applicable.
Conflict of interest
The authors declare that they are no conflict of interest.
Author details
1
LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed
Ben Abdellah University, Fez 30000, Morocco2
Chitkara College of Pharmacy, Chitkara University, Rajpura,Punjab 140401, India
3
Department of chemistry, Faculty of physical sciences, Ahmadu Bello
University, Zaria, Nigeria
4
Engineering Laboratory of Organometallic, Molecular Materials
and Environment (LIMOME), Faculty of Sciences Dhar El Mahraz, Sidi
Mohamed Ben Abdellah University, Fez 30000, Morocco
5
Laboratory of Biotechnology, Conservation and Valorization of Naturals
Resources, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben
Abdellah University, Fez 30000, Morocco6
Department of Pharmaceutical Chemistry, College of Pharmacy, King
Saud University, Riyadh, Saudi Arabia
7
Dipartimento di Chimica, Università di Torino, Torino 10125, Italy

9.

10.

11.

12.

Received: 16 May 2024 / Accepted: 15 July 2024
13.

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