Pharmacophore Generation, 3D-QSAR study, and Molecular Docking of pyrazoline derivatives for antiamoebic activity against HM1: IMSS strain of E. histolytica.

doi:10.21203/rs.3.rs-1459868/v1

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Research Article

Pharmacophore Generation, 3D-QSAR study, and Molecular Docking of pyrazoline derivatives for antiamoebic activity against HM1: IMSS strain of E. histolytica.

https://doi.org/10.21203/rs.3.rs-1459868/v1

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posted 31 Mar, 2022

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Activity against HM1: IMSS strain of E. histolytica has been carried out through the PHASE program (Schrodinger-USA). The best pharmacophore model generated consisted of five features DHHHR-4: three Hydrophobic (H), one aromatic ring (R), and one H-bond donor (D). Field-Based 3D-QSAR model studies were applied on a series (60 compounds) of pyrazoline derivatives. The best prediction was obtained with a gaussian field combined steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields (r²=0.837, q²= 0.766). The contour maps resulting from the best field-based QSAR model were exploited to discover the structural properties related to the biological activity of this series of analogous molecules. Structure-based docking studies were performed to elucidate the intermolecular interaction between pyrazoline derivatives and the 5ZFE receptor. Active ligands 35 and 36 have the highest activity and the best docking scores. The insilico ADMET screening of these active ligands was also performed and the values of all the properties are within the recommended values. The information obtained from pharmacophore, 3D QSAR model, molecular docking, and ADMET screening can be used to discover pyrazoline derivatives that increase antiamoebic activity.

Pharmacophore

Field-Based

3D-QSAR model

Docking

ADMET screening

Pyrazoline derivatives

Antiamoebic activity

E. histolytica is one of the most common parasites and is the cause of human amoebiasis. The infection occurs mainly in areas with poor sanitary conditions. Although the infection usually remains asymptomatic, the parasite can cause painful and bloody diarrhea, ulcers and can lead to abscesses in the liver, lungs, and brain. Amoebiasis is transmitted by ingestion of E. histolytica cysts in contaminated food or water. This disease represents a real health challenge, it is the third most deadly parasitic disease in the world. [1–2].

Metronidazole is the most widely used commercial drug for the treatment of amebiasis, but since E. bacteria are drug-resistant and excessive use of metronidazole leads to undesirable side effects [3–4]. Therefore, the need to find safer and more effective drugs has become urgent. [5, 6, 7].

Several heterocyclic molecules are considered to be the basis for a number of active and useful molecules, acting as deeply functionalized scaffolds [8–9]. Pyrazolines are well-known nitrogenous heterocyclic compounds, and therefore different methods have been developed for their synthesis [9].

Pyrazoline derivatives have a diversity of pharmacological activities including antibacterial, antifungal, antimicrobial, and antidepressant [10, 11]. In this regard, Mohammad Abid and co-workers investigated pyrazoline derivatives as novel substances for antiamoebic activity against E. histolytica strain HM1: IMSS by using the microdilution method, and as a result, the authors proved that pyrazoline derivatives with unsubstituted phenyl ring have IC50 in the range of 1.76–9.56 µM. [12]

The concept of QSAR methods is, as their name indicates, to set up a mathematical relationship linking in a quantitative way the molecular structure, coded by molecular properties called descriptors, with activity by using data analysis methods [13]. The aim of this method is to analyze the structural data to identify the factors intervening in the measured activity. 3D-QSAR approaches have been developed to correlate the biological activity of a series of active reference compounds with the spatial arrangement of many properties of the molecule such as steric, lipophilic, and electronic properties and to provide insights for optimization by phamacomodulation and the creation of new compounds with increased activity profiles [14].

Molecular docking is a computational strategy that can give data on intermolecular interactions of proteins, nucleic acids, lipids, and ligands. The reason for molecular docking is to obtain an optimal 3D conformation of proteins and ligands, and also relative orientation between proteins and ligands via a minimized energy-free system [15].

In the present study, the methods most used in strategies for the discovery of new molecules for therapeutic purposes are utilized. During this work, we are based on the pharmacophore model, Field-Based 3D-QSAR model, molecular docking, and in silico ADMET screening in order to guide and prioritize the synthesis of pyrazoline derivatives for antiamoebic activity against HM1: IMSS strain of E. histolytica.

Dataset

A set of 60 compounds was searched through the Pubchem database [16] based on the antiamoebic activity against HM1, including the IMSS strain of E. histolytica data. The dataset was statistically divided into a training set and a test set, considering 80% of the total compounds in the training set (48 compounds) and 20% in the test set (12 compounds). The training set was exploited to develop 3D-QSAR models, while the test set was used to validate model quality. All biological activities implemented in this study were expressed as pIC₅₀=-log10IC₅₀.

Protein preparation

The three-dimensional crystal structure of E. histolytica strain IMSS (PDB ID: 5ZEF) was obtained in PDB format from the protein database [17]. Then, the structure was developed and refined using protein preparation wizard in the Schrödinger, 2018-1 [18]. Charges and bond orders were assigned, hydrogens were inserted to heavy atoms, and all waters were removed. Applying the OPLS_2005 force field, minimization was achieved by setting the maximum RMSD of the heavy atoms to 0.30 Å.

Active Site Prediction

To determine the probable binding sites, we use SiteMap from Schrödinger, 2018-1. It identifies possible binding sites by implementing different parameters, which contribute to the tight binding of the ligand-receptor. Maps were generated using hydrophobic and hydrophilic (donor, acceptor, and metal-binding portions) maps. Sitecore and druggability score (Dscore) including parameters of site size, volume, exposure, enclosure, contact, hydrophobic, and hydrophilic character, balance (phobic/philic ratio), and donor/acceptor of hydrogen bond were employed for verification of each active site [19].

Receptor grid generation

Glide module [20] of Schrodinger software was used to generate receptor grids. Receptor grids were defined for the prepared proteins such that various ligand poses were located within the predicted active site during docking. The grids were developed with the default parameters of van der Waals scaling factor of 1.00 and charge cutoff 0.25 through the use of the OPLS 2005 force field. A cubic box of specific sizes centered around the centroid of the active site residues (the active site was found by siteMap) was created for the receptor. The bounding box was set to 10 Å × 10 Å × 10 Å for docking calculations.

Ligand preparation

We built all the molecules in version 11. 5 of Maestro [21] and prepared them using LigPrep (module implemented in the Schrodinger software) which allows us to convert the two-dimensional structure into a 3D, generate a stereoisomer, determine the most probable ionization state under default conditions at pH 7, 0 ± 2.0 using the ionizer subprogram, neutralize the charged structures, add hydrogen, and generate the energy-minimized bioactive conformers using ConfGen by applying optimized potentials for liquid simulations (OPLS) − 2005 force field.

Docking

When the receptor grid was produced; ligands were docked to the protein utilizing Glide docking protocol. Flexible ligand and XP (Extra Precision) mode, were used in the docking procedure. The docked conformers were evaluated utilizing a docking Score.

Pharmacophore generation

The pharmacophore is the set of steric and electronic characteristics of a molecule, required to realize optimal supramolecular interactions with a selected biological target and generate or inhibit a biological response [22]. It can also be said that the 3D pharmacophores describe the spatial arrangement of chemical properties required for biological activity from a set of reference active ligands [23]. In this research paper, we have chosen the method "ligand- based" pharmacophore models on 60 ligands of pyrazoline derivatives for generate a common pharmacophore hypothesis. These ligands were divided into active and inactive forms by activity threshold value. By defining the pharma set, compounds with pIC50 > 6 were considered as active, since those with pIC50 < 5.5 are inactive. Pharmacophore points can be based on atoms, topological groups, and chemical properties of groups of atoms. pharmacophore points can describe chemical properties of groups of atoms, which are essential for establishing interactions with the binding site. These properties are mainly hydrogen bond acceptors (A) and donors (D), positively (P) or negatively (N) charged groups, aromatics ring (R), hydrophobic groups(H). [24] PHASE module [25] of Schrodinger software was used to generate the pharmacophore model.

In this study, the ligand-based pharmacophore, field-based 3D-QSAR model, and docking studies on pyrazoline derivatives were performed to discover the structural characteristics of antiamoebic activity. The code and activity of these inhibitors (experimental and predicted) are shown in Table 1.

Pharmacophore studies

A total of 5 different variant hypotheses were created at the end of the common pharmacophore identification treatment. A maximum of six features allowed to develop a hypothesis and a number of CPHs (Common Pharmacophore Hypothesis) was reported to be common to all molecules based on hydrogen bond donor /acceptor (D)/(A), hydrophobic (H), and aromatic ring (R). There were five hypotheses based on the combination DHHHR, two hypotheses based on HHHRR, two hypotheses based on AHHRR, and one hypothesis based on ADHHR. We have selected those of the pharmacophore models whose phase scores ranked in the top (Table II). The top model was found to be associated with the five-point hypothesis (Fig. 1), which consists of three hydrophobic (H), one aromatic rings (R), and one hydrogen bond donor(D). The actives ligands have the highest activity as shown in Fig. 2. The alignment of all active ligands with their pharmacophoric features is shown in Fig. 3. The score of all hypotheses was presented in Table 2.

Field-Based 3D-QSAR model

Using the field-based QSAR method (in Schrodinger software) to create a correlation model between the available activity values and the 3D properties of a set of aligned compounds. There is the ability to display the QSAR model in the workspace for qualitative evaluation, which would allow us to add or remove functional groups and use the model to predict the activities of other molecules. The 3D QSAR model was developed in the Schrodinger software phase module taking 48 compounds for training, while the model was validated using 12 compounds as a test set with six PLS factors. The PLS method shows good statistical values and predictions for the data set ( see Table 3). The statistical values of the QSAR method show a better regression coefficient of R2 (0.837), a large value of F (35.3) and P (1.11e-014), a small value of standard deviation (0.135), RMSE (0.17), and high values of Pearson-P (0.913) indicate a statistically significant regression model. The model was validated by the cross-validation correlation coefficient q2 = 0.766.

The graph of the biological activity obtained by comparison with the predicted biological activity of the training and test sets is shown in Fig. 4. The relative contributions of steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor fields are 0.332, 0.182, 0.328, 0.118, and 0.052, respectively (see Table 4 for the best PLS). The effective validity of the model is indicated by its ability the predict biological activity for new molecules. A close analysis of different validity tests indicates that the model created by the Field-Based QSAR analysis is very good. The experimental and predicted activities for the training set and test set are given in Table I. From Table 4, it appears that the electrostatic, H-bond Acceptor, and H-bond Donor fields contribute less compared with steric and hydrophobic fields on the biological reactivity.

The field-based QSAR contours for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties are shown in Fig. 5a to Fig. 5e, ,respectively. In the contour maps, each colored contour corresponds to specific properties. In the steric field, green contours show regions where voluminous groups increase biological function, while yellow contours show regions where voluminous groups decrease activity. In the electrostatic field, the red contours correspond to regions favored by positive electrostatics, but the blue contours indicate regions favored by negative electrostatics. The yellow contours relate to hydrophobically favored regions, while the white contours correspond to hydrophobically unfavored regions. The magenta and red contours correspond to favorable and unfavorable regions for hydrogen bond acceptors, respectively, while the purple and cyan contours represent favorable and unfavorable regions for hydrogen bond donor groups, respectively.

The field-based steric QSAR in Fig. 5a shows no presence of yellow contours on the molecule, revealing the absence of unfavorable regions. The presence of large green contours on the adamantine, pyrazoline, and benzene rings indicates a significant steric in these regions. Therefore, voluminous substituents in these regions increase the activity. The electrostatic contour plots of the field-based QSAR are shown in Fig. 5b. The blue contour denotes the area where positive groups are required for high activity, while the red area corresponds to a region favorable for negative groups. The hydrophobic contour plots from the field-based QSAR are shown in Fig. 5c. The white contours indicate regions of unfavorable hydrophobic interaction near the adamantine and pyrazoline rings. The yellow contours are located near the benzene ring, indicating that any voluminous groups at this position correspond to favorable hydrophobic regions. The hydrogen bond donor contour maps of the field-based QSAR are shown in Fig. 5d. The contour maps reveal the favorable (purple) and unfavorable (cyan) portions of the hydrogen bond donors. The purple contours are visualized near the adamantine ring attached to the nitrogen atom, indicating that the hydrogen bond donor functionalities in this area increase activity. The hydrogen bond acceptor fields of the field-based QSAR (Fig. 5e) are indicated by red and magenta contours, the red contours correspond to the parts where hydrogen bond acceptors are favorable, and the magenta contours indicate the parts where hydrogen bond acceptors on inhibitors are unfavorable for activity. In this position (red), any substituent containing an acceptor group increases activity.

Docking studies

Binding site examination was carried out using the SiteMap tool of Schrodinger software. SiteMap found five sites from the site score that comprises size, volume, amino acid exposure, enclosure, contact, hydrophobicity, hydrophilicity, and donor/acceptor ratio. The sites with a site score of 1 and above can be an ideal site for the ligand binding. The best level of potential receptor binding sites was identified using SiteMap. The best site had a score of 0.99 site score, 1.02 Dscore, 234.3 volume score, 3.74 hydrogen bond donor/acceptor score, 0.87 hydrophilic score, and 0.56 hydrophobic score. The active site residues were identified as PHE292, LEU291, VAL266, HIE264, LEU263, LYS261, MET260, LYS259, GLY258, LEU90, ASN89, ASN88, VAL87, PHE3, MET1, GLN2, VAL41, and GLN40, respectively. So active site 1 is most likely to do the docking process.

Docking studies were executed to know the intermolecular interaction of the ligand with the targeted enzyme by applying Glide module [22]. The docking technique was performed to study the binding mode of active ligands on the receptor (5ZEF) and to obtain information for further optimization of the structure. Grid generation to determine the binding site on the receptor was performed via the receptor grid generation panel with default settings. Glide XP was used for docking purposes. To estimate the docking of protonated ligands, the docking score should be considered. Thus, in this work, a docking score was applied to compare the stability of the simulated complexes. The average docking score obtained for the potential inhibitors 14, 32, 33, 35, 36 and 55 were − 4.03, -3.538, -3.535, -5.176, -4.96 and − 4.34, respectively. Then, the inhibitor 35 / 5ZEF complex is more stable than inhibitors 14 / 5ZEF, inhibitors 32 / 5ZEF, inhibitors 33 / 5ZEF, inhibitors 36 / 5ZEF, and inhibitors 55 / 5ZEF complexes. Among these inhibitors studies, inhibitor 35 of them had a docking score higher in comparison with all active ligands. The results of docking analysis were described in Table 5 and Fig. 6.

Docking studies of the ligands 32 and 33 have shown that the nitrogen atom (NH) acts as a hydrogen bond donor and makes a hydrogen bond to the PHE292 for both structures. For ligands 35, the benzene ring (contain two fluorine atoms) of the ligand makes π-π interactions with PHE 3 residue. For ligands 36, the benzene ring (contain two fluorine atoms) of the ligand makes π-π interactions with PHE 3 residue, and the nitrogen atom (NH) acts as a hydrogen bond donor and makes a hydrogen bond to the LEU263. For ligands 14, the benzene ring (contain the CH₃ group) of the ligand makes π-π interactions with PHE 3 residue and the O atom of the amide group makes a hydrogen bond acceptor to the HIE 264. For ligands 55, the benzene ring (contain a chlorine atom) of the ligand makes π-π interaction (π –cation) with LYS 261. Those overall interactions with PHE 3 could be the reason for the high docking score and hence, it promises to be potent and also selective for antiamoebic activity.

ADMET properties

The ADMET (absorption, distribution, metabolism, elimination, toxicity) analytical properties of the compounds can be determined in-silico using the qikprop module of the Schrödinger Suite 2018. In this way, we evaluated the physiochemical descriptors and pharmaceutically relevant properties of the active ligands to analyze the drug properties (Tables 5a and 5b).

The computed number of likely metabolic reactions (# metab) is in the range of 1–6. All the active ligands showed good partition coefficient (QP log Po/w) values (4.7 to 5.77), which were critical for the absorption and distribution of drugs. The approximate number of hydrogen bonds that would be given by the solute to water molecules in an aqueous solution of the compounds is in the range of 0 to 1. The estimated number of hydrogen bonds that the solute would accept from water molecules in an aqueous solution of the compounds is in the range of 3 to 7. The number of violations of Lipinski's rule of five is in the range of 0 to 1. The compounds have 100% of % Human Oral Absorption. The estimated brain/blood partition coefficient (QPlogBB) is in the range of 0.713 to 0.865. Thus, almost all the properties of the active ligands are within the recommended values. From these results, we can say that the active ligands can be used in clinical trials due to good ADME properties (absorption, distribution, metabolism, and excretion). The details of the ADMET properties for active ligands are shown in Tables6a and 6b.

In this study, the developed pharmacophore model DHHHR-4 implicated the role of three hydrophobic (H), one aromatic ring (R), and one H-bond donor (D) in biological activity against HM1: IMSS strain of E. histolytica. Field-based QSAR model analyses were applied to evaluate the antiamoebic activity of a set of pyrazoline derivatives. QSAR models showed the best statistical results in terms of r2 and q2 values (r2 = 0.837, q2 = 0.766). The effects of steric electrostatic, hydrophobic, and hydrogen-bond donor/acceptor fields on biological activity were explicated by analyzing the Field-Based QSAR contour maps. The results appear that the steric and hydrophobic fields contribute to reactivity more than electrostatic, H-bond acceptor, and H-bond donor fields, respectively. Docking was used to studying the binding mode of the active ligands on the 5ZEF receptor to obtain information for further optimization of the structure. Ligand 35 (PubChem ID: 103630643) shows the best docking score. The insilico ADMET screening of these active ligands was also performed and the values of all the properties are within the recommended values. The results obtained in this study present an approach to predict the affinity of pyrazoline derivatives in relation to antiamoebic activity.

Acknowledgements

Author Bouacha Samir is thankful to prof. Fawzi Dahdouh for improving the language of this paper.

Conflict of interest The author declares that there are no conflicts of interest regarding the publication of this paper.

Funding Not Applicable

Code Availability Not Applicable

Ethical statement This article does not contain any studies with human participants or animals performed by the author.

Data availability statements All data generated or analyzed during this study are included in the article and supporting information.

Ethics Approval Compliance with ethical standards

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Table 1.Predicted activity data for training and test set of ligands.

Serial numbers

Ligand code

( pubchem)

QSAR set

Activity

PLS factors

Predicted activity

Pharm set

160705969

160700698

160700697

160695313

160695312

160695311

160695310

160695309

160679154

160679153

160679151

160673849

160673848

160668514

160668513

103654028

103558948

103546774

103516066

103516057

103516047

103516026

103516024

103516017

103516016

103524288

103524253

103189534

103166159

160679150

103630542

103630646

103630645

103630644

103630643

103630642

103630641

103630592

103630591

103630590

103630589

103630588

103630587

103630545

103630544

103630543

103630541

103630540

103630479

103630478

103630477

103630476

103630475

103630474

103630426

103630425

103630424

103630423

103630422

103630421

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

Test

Training

5.434

5.580

5.546

5.966

5.92

5.535

5.519

5.282

5.58

5.332

5.27

5.31

5.89

6.065

5.582

5.316

5.377

5.322

5.27

5.292

5.358

5.322

5.206

5.189

5.135

5.226

5.220

5.74

4.89

5.294

5.752

6.328

6.236

5.747

6.292

6.173

5.754

5.619

5.273

5.019

5.551

5.474

5.300

5.774

5.417

5.302

5.556

5.208

5.536

5.352

5.136

5.43

5.279

5.226

6.050

5.962

5.357

5.636

5.549

5.428

5.716

5.645

5.708

5.906

5.764

5.832

5.390

5.333

5.781

5.354

5.712

5.647

5.715

5.711

5.646

5.269

5.314

5.326

5.257

5.215

5.315

5.270

5.320

5.270

5.328

5.260

5.220

5.840

5.333

5.365

5.654

6.236

6.288

5.843

6.107

6.152

5.730

5.824

5.227

5.125

5.659

5.698

5.299

5.662

5.707

5.244

5.700

5.259

5.435

5.429

5.383

5.336

5.340

5.312

5.840

5.882

5.725

5.524

5.533

5.363

INACTIVE

ACTIVE

INACTIVE

ACTIVE

INACTIVE

ACTIVE

INACTIVE

Table 2

Score of different parameters of the Common Pharmacophore Hypothesis.
CPHs	Phase. score	Survival score	Vector score	Volume score	Site.score	Num Matched
DHHHR-4	0.785	4.183	0.464	0.649	0.445	4
DHHHR-3	0.769	4.188	0.480	0.644	0.452	4
DHHHR-2	0.765	4.197	0.458	0.656	0.457	4
DHHHR-1	0.764	4.215	0.480	.644	0.452	4
DHHHR-5	0.760	4.170	0.451	0.652	0.455	4
ADHHR-1	0.519	4.152	0.635	0.615	0.463	4
HHHRR-1	0.447	4.708	0.836	0.717	0.567	3
AHHRR-1	0.431	4.652	0.866	0.702	0.628	3
HHHRR-2	0.354	4.611	0.837	0.685	0.546	3
AHHRR-2	0.318	4.379	0.759	0.669	0.547	3

Table 3

Statistical values for 3D-QSAR model generated by PLS.
# Factors	SD	R²	R² cv	R² scramble	stability	F	P	RMSE	Q2	Pearson-r
1 2 3 4 5 6	0.260 0.241 0.211 0.178 0.149 0.135	0.328 0.436 0.576 0.704 0.798 0.837	0.177 0.178 0.094 0.181 0.364 0.311	0.152 0.276 0.342 0.396 0.455 0.514	0.970 0.914 0.770 0.684 0.652 0.534	22.5 17.4 20.0 25.6 33.3 35.3	2.04e-005 2.48e-006 2.58e-008 6.79e-001 1.38e-013 1.11e-014	0.30 0.28 0.23 0.16 0.21 0.14	0.284 0.355 0.581 0.781 0.632 0.766	0.670 0.749 0.860 0.911 0.816 0.913

Table 4

Details of Field-Based QSAR model calculations.
#Factors	Guassian Steric	Guassian Electostatic	Guassian Hydrophobic	Hbond Acceptor	Hbond Donor
1 2 3 4 5 6	0.537 0.490 0.416 0.362 0.340 0.332	0.071 0.084 0.111 0.142 0.173 0.182	0.249 0.247 0.302 0.335 0.328 0.328	0.114 0.137 0.106 0.108 0.103 0.118	0.026 0.039 0.063 0.050 0.053 0.052

Table 5. Results of docking analysis of actives ligands.

Ligands & code

Docking Score

Glide Score

Glide Energie

14 (160668514)

32 ( 103630646)

33 (103630645)

35 (103630643)

36 (103630642)

55 ( 103630426)

-4.03

-3.538

-3.535

-5.176

-4.96

-4.34

-4.03

-3.538

-3.535

-5.176

-4.96

-4.34

-41.3

-35.62

-33.45

-34.5

-37.8

-34.73

Table 6

**a/6b.** In silico ADME screening for selected ligands.
Ligands code	Donor HB	Accpt HB	logP o/w	# metab	QPlog Khsa	Rule of Five	% Human Oral Absorption
160668514	0	7	5.05	6	0.80	1	100
103630646	1	3	5.68	1	1.06	1	100
103630645	1	3	5.77	1	1.09	1	100
103630643	1	3	5.29	1	0.68	1	100
103630642	1	3	5.23	1	0.70	1	100
103630426	0	3	4.7	1	0.63	0	100
Recommended values	≤ 5	≤ 10	–2.0–6.5	1–8	–1.5–1.5	max 4	> 80% is high < 25% is poor

Ligands code	QPlogBB	QPlogHERG	QPlogKP	QPPlog caco	QPlogMDCK
160668514	-0.731	-6.80	-1.810	1051.6	522.35
103630646	0.68	-5.28	-1.138	7402.15	10000
103630645	0.706	-5.30	-1.126	7504.09	10000
103630643	0.865	-6.136	-0.640	7139.7	10000
103630642	0.737	-5.86	-0.923	5352.76	10000
103630426	0.713	-4.37	-1.330	6963.7	10000
Recommended values	-3.0 to 1.0	Above − 5	-8 to1	≤ 25: poor; ≥ 500: great	≤ 25: poor; ≥ 500: great

DonorHB - Estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution,

AccptHB- Estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution,

QPlogPo/w - Predicted octanol/water partition coefficient,

#metab- Number of likely metabolic reactions,

QPlogKhsa- Prediction of binding to human serum albumin,

RuleOfFive Number of violations of Lipinski’s rule of five,

%Human- oral absorption- Predicted human oral absorption on 0 to 100% scale.The prediction is based on a quantitative multiple linear regression model.

QPlogKp: Predicted skin permeability, log Kp (acceptable range is: –8.0 –1.0)

QPlogKhsa: Prediction of binding to human serum albumin (acceptable range is: –1.5 – 1.5)

QPlogBB: Predicted brain/blood partition coefficient

QPPMDCK: Predicted MDCK cell permeability in nm/sec .

No competing interests reported.

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Pharmacophore Generation, 3D-QSAR study, and Molecular Docking of pyrazoline derivatives for antiamoebic activity against HM1: IMSS strain of E. histolytica.

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Abstract

Figures

Introduction

Matrials And Methods

Dataset

Protein preparation

Active Site Prediction

Receptor grid generation

Ligand preparation

Docking

Pharmacophore generation

Results And Discussion

Conclusion

Declarations

References

Tables

Competing Interests

Supplementary Files

Status:

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