1.0 INTRODUCTION:

Dementia being the most perceived kind of Alzheimer's disease is now a powerful neurodegenerative issue which is regularly seen in older people.¹ One of the basic highlights of Alzheimer's illness is degeneration of cholinergic neurons, situated in the basal forebrain cores, dynamically denying the cerebrum of its cholinergic information.²The etiology of Alzheimer's ailment being unknown, yet the impact of the illness procedure prompts neuronal injury.³The major biochemical variation from the norm of AD happens in the medial lobe (hippocampus) and cerebral cortex as a decrease of protein enzyme cholineacetyl transferase. The lack of this protein causes decline in Ach in the cerebrum.⁴ Two trademark histopathology highlights connected to AD are an expansion in amyloid plaques and high thickness of neurofibrillary tangles.⁵

Aß is the centre of neuritic plaques and is a section from APP which may cause inflammation followed by cell death. Inflammation and free radicals in later AD ends up in degeneration of neurons resulting in AD.⁶In late days, AchE inhibitors⁷ and NMDA partial agonist are considered for the treatment. A more secure way to handle such conditions would be utilization of natural anti-cholinesterase.⁸

Syzygium aromaticum (Myrtaceae), a tropical evergreen plant, broadly utilized as spice, seasoning agent and in human nutrition.⁹ Studies had described the remedial properties of clove, for example, sexual enhancer, stomachic, carminative, antispasmodic, mitigating, cancer prevention agent, anti-hyperglycemic, against stress, antimutagenic, and allelopathic, anti-coagulant¹⁰ antiseptic and anesthetic to toothache among different pain.¹¹ In any case, there are studies suggesting the impact of clove on the neuronal activities. By this manner, the primary point of this work is to predict the potential constituent of clove that produces multi target action in Alzheimer's disease.¹²

2.0 MATERIALS AND METHODS:

2.1 Virtual screening analysis:

2.1.1 Preparation of Target Protein:

The focus was on target proteins of AD such as Alzheimer's disease amyloid β-peptide (PDB ID: 1IYT), Binary complex structure of human tau protein kinase I(PDB ID: 1J1C), Crystal structure of human butyryl cholinesterase (PDB ID: 1P0I), BACE1 inhibitor (PDB ID:4B05), Crystal Structure of Human Acetyl cholinesterase (PDB ID:4PQE), Crystal structure of the E-1 domain of the amyloid precursor protein (4PWQ), Paired helical filament in Alzheimer's disease brain (PDB ID: 503L) were retrieved from RCSB PDB in. pdb design. They were streamlined by means of arranging hetero atoms and the first conformer of the proteins was considered for docking assessment. Consequently, the conformer was made with explicit number of atoms and residues shifting for each target protein.

2.1.2 Preparation of Ligands:

The ligands were chosen dependent on the literature from Pubchem database literature from Pubchem database and the drug likeness property. The focused ligands were then sketched for their 2D structure using Chem Draw 16.0 PerkinElmer software. Each ligand was then optimized using molecular mechanics of Chem3D 16.0 PerkinElmer software for energy minimization followed by geometric conformation. The output structures were then saved in the. pdb format.

2.1.3 Molinspiration Cheminformatics:

Molinspiration offers expansive scope of Cheminformatics programming apparatuses supporting standardization of atoms, atom fracture, estimation of different sub-atomic properties required in QSAR and so on. Molinspiration underpins significant sub-atomic properties such as log P, number of hydrogen bond donors and acceptors, polar surface area and many others, just as forecast of bioactivity score for the most significant medication targets (ion channel modulators, GPCR ligands, nuclear receptors, and kinase inhibitors).¹³Those compounds which successfully passed the characteristic drug-likeness property with adequate bioactivity scores for targets of AD were subjected to docking studies.¹⁴

2.1.4 Docking using PyRx:

2.1.4.1 Preparation of ligand:

The structure of the chosen ligands was portrayed in a 2D manner and optimized and stored in the format. pdb. The focused ligands in .pdb format were then converted to. pdbqt format via Open Babel version 2.4.1 software for running Autodock Vina in PyRx tool.

2.1.4.2 Preparation of target protein:

The. pdb file format of optimized target proteins (PDB ID: 1IYT, 1J1C, 1P0I, 4B05, 4PQE, 4PWQ, 503L) was also converted into the. pdbqt format using the molecular file format converter Open Babel version 2.4.1 software tool.

2.1.4.3 Grid parameters:

The grid box was built for running Autodock vina on optimised target proteins and its parameters including centre (x,y,z) and its dimensions are depicted in the figure. The ligands being optimized were then subjected to docking analysis using Autodock vina wizard using PyRx- Python Prescription 0.8 software against the optimized target proteins.¹⁵ After docking, the results of binding affinity were noted.

2.1.5 Docking using MVD:

2.1.5.1 Preparation of Ligand:

The ligands that has been optimized and stored in .pdb format are used here for docking analysis.

2.1.5.2 Preparation of receptor:

The X-ray beam crystal co-ordinates of different objective proteins (PDB ID: 1IYT, 1J1C, 1P0I, 4B05, 4PQE, 4PWQ, 503L) were recovered from RCSB PDB. Then appropriate bonds, bond requests, hybridization and charges were assigned to it utilizing MVD. The potential binding sites were determined utilizing the built-in cavity detection algorithm executed in MVD. The inquiry space of docking was contemplated as a subset area of 25.0 Angstroms around the dynamic side separated.

2.1.5.3 MVDs docking search algorithms:

The scoring limit of Moldock relies upon the Linear Potential- Piecewise, which is a revamped potential whose parameters are fit to protein-ligand structures and a coupling data scoring limit that is also loosened up in GEMDOCK (Generic Evolutionary Method for nuclear DOCK) with another hydrogen holding term and charge plans.¹⁶

2.1.5.4 Mol Dock score: The H-bonding which involves potential H-donors and acceptors was used to check the hydrogen bond directionality. The coupling site on the protein was characterized as reaching out in X as well as Y and also Z directions around the chose cavity with a span of 25 Angstroms. Ten runs of each docking examination were performed and the Moldock score was resolved.

2.1.6 Docking using Hex 8.0:

The docking of the chosen targets with the optimised ligands was performed by utilizing Hex 8.0 programming tool. The receptor and the ligand particle were stacked from open choice then the hydrogen atoms that are polar in nature were inserted into the ligands. The matrix projections were chosen dependent on the dynamic build ups of the targeted protein. The docking was started by choosing docking choice from the control option.¹⁷ The Hex message box demonstrated the E-total scores which is the best binding energy or binding affinity of the ligand with that receptor. Subsequently, the E-total score with the interaction of protein with ligands in 2D view was obtained.¹⁸

2.1.7 Docking using AutoDock4.2:

2.1.7.1 Preparation of Receptors and ligands:

The optimized target proteins (PDB ID: 1IYT, 1J1C, 1P0I, 4B05, 4PQE, 4PWQ, 503L) saved in the .pdb format along with the 2D optimized structure of the focused ligands saved in.pdb format was used for Autodock docking analysis.

2.1.7.2 Grid and Docking parameters:

AutoDock 4.0 was propelled in a Cygwin interface in the Windows working system. Docking logs were broke down in the graphical UI of ADT (Auto Dock Tools). Gasteiger charge was appointed to the ligand. Rigid roots were allocated to the ligand and the "active" or rotatable bonds were assigned to be five.¹⁹ The altered structures represent the flexibility of bonds and were saved in .pdbqt format. The grid size was set around 60*60*60 revolved around the anticipated cavities with a default grid point spacing of 0.375 Å. They were acquired utilizing Auto Grid with Lamarckian algorithm.²⁰

2.1.8 TEST software:

The smiles of the compounds to be evaluated are entered into the TEST software and the rat oral LD50 values are recorded under several advanced QSAR methodologies such as Hierarchical method, Consensus, Group contribution, FDA method, Single model, and nearest neighbour.

2.2 IN-Vitro analysis:

The Oleanolic acid (OA), 2, 2_-diphenyl-1-picrylhydrazy, also known as DPPH, Tertiary butyl hydroquinone (TBHQ), bovine serum albumin (BSA) and the vast majority of different chemicals and reagents utilized in this investigation were acquired from Sigma- Aldrich Company Pvt Ltd. All the chemicals utilized were of high purity.

2.2.1 Anti-oxidant activity by DPPH assay:

The DPPH compound can produce stable radicals in saturated Methanol solution. If the compound has radical scavenging activity, it will decrease the intensity of absorbance measured.²¹ The examination was done as follows: 3mL of methanol was added to 150µL solution of 3.3mM DPPH and 100µL of the sample (sample dissolved in DMSO 1mg/ml) at various concentrations. Stir the mixture and incubate for 15mins. After incubation for 1/2, 1 and 2 hrs, measurement of absorbance was done at 517nm against the control. The positive control was TBHQ and the evaluation was done in form of triplicates. The percentage of DPPH radical scavenged can be determined utilizing the following equation:²²

Percentage Inhibition= [Control _Abs- Sample_Abs/Control _Abs] x 100

2.2.2 Anti-oxidant activity by FRAT assay:

The antioxidant activity of OA was measured according to Gülçin et al.²³ The reduction taking place from Fe+3 to Fe+2 was assayed by measuring the absorbance at 700nm. 100μL of OA (1mg/ml) was taken and mixed with 1% K3 [Fe (CN) 6] (1ml) and sodium PO4 buffer 1ml (0.2 M at pH 6.6). Incubate for 20 min, and then acidify it by adding 1mL 10% of trichloroacetic acid followed by 250μL of 0.1% of ferric chloride and the absorbance was recorded at a wavelength of 700nm.²⁴

Percentage Inhibition= [Control _Abs- Sample_Abs/Control _Abs] x 100

2.2.3 Inhibition of Albumin Denaturation:

The inflammation inhibition activity was done using methods from Sakat et al with fewer modifications.²⁵ The sample (varying concentrations) was added to 1% BSA in aqueous form. 1 N of hydrochloric acid was used to adjust the pH to 6.3. At 37°C, incubate it for 15mins followed by heating for 5mins at 70°C. Cool the samples and then measure the absorbance at 660nm and performed in triplicates.

The Protein denaturation inhibition percentage was calculated from

Percentage Inhibition= [Control_Abs- Sample_Abs/Control _Abs] x 100

2.2.4 Anti-Protease activity:

The Oyedepo et al and Sakat et al method with less modification was used here.²⁶ 0.06mg trypsin (2ml), Tris HCl buffer 20mM (1ml at pH 7.4), sample (1ml of different concentrations) was incubated for 5 min at 37^oC . 0.8% (w/v) casein (1ml) was added and incubated for 20mins. 70% per chloric acid (2ml) was added and then the suspension was centrifuged. The supernatant was measured for absorbance at 210nm and was done in triplicate. The proteinase inhibitory activity percentage was calculated:

Percentage Inhibition= [Control_Abs- Sample_Abs/Control _Abs] x 100

The Data is expressed here in the form of mean ±standard deviation (SD). All tests were done utilizing Graph Pad Prism and the variation between experimental groups was compared by ANOVA.

3.0 RESULTS AND DISCUSSION:

Acknowledgment of the capacity of commonly available natural items having a good impact on wellbeing is now developed enthusiasm for determining benefits of utilizing natural product that promises in health-promoting properties. Hence, this results in new drug development for its medicinal purposes. For instance, the advantages of naturally available items have now been effectively illustrated in AD.²⁷ Inspite of knowing that its etiology stays obscure, the Neuro-pathological profile of AD is related with memory loss and plaques formation. This causes outburst of free radicals and inflammation resulting in degeneration in the forebrain of basal region.²⁸ This study aims to identify phytoconstituents that has the ability to elicit a multi-target action over AD targets. Besides, at the sub-atomic level, patients influenced by AD shows storage of Amyloid beta, spiral filaments in neurons along with enhanced oxidative stress and inflammation.²⁹ In addition, Ach, glutamate, and serotonin like other neurotransmitters are also affected in the delayed phase of AD. Henceforth, the treatment methodologies available are essentially symptomatic.³⁰ A few of the cholinesterase inhibitors are presently being used such as donepezil, tacrine³¹, rivastigmine, and galantamine, which has many Side effects. Therefore, there is a developing logical intrigue in recognizing natural sources for the treatment of AD.³² Computational studies enroutes us to find compounds and analyse their binding affinty to focused protein.³³ The present study deals with the investigation of the phytoconstituents from the plant Sygyzium aromaticum for a MTA of Alzheimer’s disease.

3.1 Drug likeness Property analysis:

From the Table 1, it is evident that only few phytoconstituents had the drug likeness property as their scores were found to be in range (milogP(<=5); TPSA(<=140 Å²); MW(<=500 Daltons); nON(<=10); nOHNH(<=5); nrotb(<=10)). As the ligands are directed towards multi-target action, the selection of constituents was also based on their bioactivity scores as shown in Table 2, primarily based on Enzyme inhibition, Protease inhibition and kinase inhibition. Hence, the bioactivity scores of the eight phytoconstituents (Biflorin (1), Campesterol (5), Kaempferol (6), Rhamnocitrin (7), Rhamnetin (8), Isoengeletin (9), Oleanolic acid (10), Isobiflorin (12)) were found to be significantly producing inhibition. Hence, these eight ligands were considered for docking analysis.

Table 1. Drug likeness property of phytoconstituents

S. No	Ligand Name	Mi logp	TPSA	natoms	MW	noH	noHNH	nvoi	nrot	Voume
1	Biflorin	-0.7	160.8	25	354.3	9	6	1	2	292.34
2	Casuarictin	2.1	444.1	67	936.6	26	15	3	3	707.18
3	Eugeniin	2.43	444.1	67	938	26	15	3	9	718
4	Tellimagrandin I	1.2	385.2	56	786.5	22	13	3	9	606.3
5	Campesterol	8.3	20.23	29	400.6	1	1	1	5	439.7
6	Kaempferol	2.17	111.1	21	286.2	6	4	0	1	232.07
7	Rhamnocitrin	2.71	100.1	22	300.2	6	3	0	2	249.59
8	Rhamnetin	2.22	120.3	23	316.2	7	4	0	2	257.61
9	Isoengeletin	0.5	166.1	31	434.4	10	6	1	3	362.17
10	Oleanolic acid	6.72	57.5	33	456.7	3	2	1	1	471.14
11	Stigmasterol-3-O-beta-D-glucopyranoside	6.16	99.38	41	574.8	6	4	2	8	582.45
12	Isobiflorin	0.70	160.8	25	354.3	9	6	1	2	292.34
13	Strictinin	0.31	310.6	45	634.4	18	11	3	3	487.20
TPSA- Total Polar Surface Area; MW- Molecular Weight; nvoi- Number of Violations

Table 2. Bioactivity score of the phytoconstituents

S. No	Ligand Name	GPCR ligand	Ion ligand	Kinase inhibitor	Nuclease ligand	Protease inhibitor	Enzyme inhibitor
1	Biflorin	-0.09	-0.14	-0.20	0.01	-0.08	0.4
2	Casuarictin	-3.4	-3.6	-3.6	-3.6	-3.1	-3.5
3	Eugeniin	-3.4	-3.6	-3.6	-3.6	-3.1	-3.5
4	Tellimagrandin I	-1.5	-2.7	-2.2	-2.3	-1.5	-1.8
5	Campesterol	0.11	0.01	-0.48	0.71	0.01	0.50
6	Kaempferol	-0.1	-0.2	0.21	0.32	-0.27	0.26
7	Rhamnocitrin	-0.12	-0.28	0.19	0.30	-0.28	0.20
8	Rhamnetin	-0.11	-0.27	0.21	0.27	-0.27	0.20
9	Isoengeletin	0.10	0.05	0.03	0.11	0.17	0.34
10	Oleanolic acid	0.28	-0.06	-0.40	0.77	0.15	0.65
11	Stigmasterol-3-O beta-D-glucopyranoside	0.14	-0.30	-0.45	0.34	0.04	0.42
12	Isobiflorin	-0.08	-0.33	-0.16	0.01	-0.09	0.39
13	Strictinin	-0.11	-0.71	-0.45	-0.44	-0.03	-0.15

Table 3. MVD (Molecular Virtual Docking) Results

Receptor	Scores	1	5	6	7	8	9	10	12
1iyt	MD	-40.6	-51.6	-61.1	-63.9	-61.3	-52.1	-49.4	-44.64
	RE	-45.6	-32.5	-53.9	-54.5	-53.0	-29.0	-36.3	-28.64
	HB	-6.44	0	-0.90	-2.5	-0.6	0	-5.45	-4.52
1jlc	MD	-55.8	-81.7	-60.9	-71.3	-61.5	-77.2	-63.6	-68.36
	RE	-62.6	-60.4	-54.6	-63.7	-50.1	-59.5	-52.7	-61.12
	HB	-12.5	-9.86	-8.32	-10.6	-7.4	-7.5	-10.6	-20.49
1poi	MD	-89.6	-97.5	-95.1	-98.2	-100	-111	-118	-102.3
	RE	-89.0	-88.1	-88.8	-40.9	-87.9	-37.7	-109	-91.77
	HB	-9.31	-56.7	-4.90	-7.17	-4.97	-2.5	-15.6	-11.34
4bo5	MD	-71.7	-100	-91.5	-62.9	-70.1	-84.4	-72.6	-67.83
	RE	-68.5	-82.6	-70.3	-56.0	-64.6	-82.8	-71.7	-62.71
	HB	-8.59	0	-1.34	-9.37	-8.53	-3.1	-12.3	-10.68
4pqe	MD	-101	-127	-117	-125	-125	-45.1	-132.	-128.8
	RE	-58.5	52.7	-100	-105	-104	-35.9	-108	-113
	HB	-14.2	-5.72	-6.85	-6.81	-3.51	-7.08	-11.6	-19.21
4pwr	MD	-88.8	-97.0	-83.0	-85.8	-93.1	-78.3	-104	-85.61
	RE	-85.2	-76.8	-74.2	-76.0	-72.1	-7.33	-82.4	-73.42
	HB	-12.5	-2.5	-5.33	-5.48	-10.2	-5	-23.0	-8.42
503l	MD	-17.7	-55.6	-47.7	-46.5	-46.3	-45.0	-51.8	-52.09
	RE	-23.5	-29.1	-41.5	-40.8	-40.6	-32.7	-47.6	-43.48
	HB	-2.68	0	-3.84	-0.14	-0.14	-2.5	-9.30	-6.52
Total Binding Socre	Moldock Score	-465.9	-611.7	-557.7	-553.4	-557.4	-416.2	-592.1	-462.88
MD-MolDock score; RE- Re rank score; HB- Hydrogen bonding

Table 4. HEX 8.0 Docking Results (E-values)

Ligand	1IYT	1J1C	1P0I	4B05	4PQE	4PWQ	503L	Total Binding Energy
1	-219.48	-268.68	-291.65	-244.96	-276.32	-240.94	-213.44	-1755.47
5	-237.19	-311.99	-327.43	-295.21	-256.84	-256.84	-243.86	-1929.4
6	-239.46	-233.68	-257.59	-299.34	-248.47	-239.36	-23.46	-1757.36
7	-245.5	-259.07	-265.85	-228.18	-275.44	-259.86	-238.51	-1772.11
8	-255.16	-252.64	-252.64	-227.94	-298.9	-260.39	-257.50	-1805.17
9	-229.82	-289.94	-321.75	-287.57	-308.9	-274.04	-232.90	-1936.92
10	-216.10	-301.88	-341.59	-320.89	-275.72	-263.93	-230.11	-1949.21
12	-207.19	-264.06	-281.01	-244.20	-274.30	-241.99	-231.50	-1744.25

Table 5. PyRx Docking Results (Kcal/mol)

LIGAND	1IYT	1J1C	1P0I	4B05	4PQE	4PWQ	503L	Total Binding Energy
1	-7.3	-6.9	-10.4	-7.8	-7.8	-6.4	-4.9	-51.5
5	-7.3	-8.6	-6.3	-9.3	-8.3	-6.8	-6.3	-52.9
6	-6.1	-8.8	-7.8	-8.2	-8.7	-7.5	-5.7	-52.8
7	-7.5	-8.4	-10.9	-8.0	-9.1	-7.6	-5.5	-57
8	-6.8	-8.1	-7.5	-8.4	-8.7	-6.9	-5.5	-51.9
9	-7.1	-8.6	-7.4	-9.1	-7.9	-7.2	-5.7	-53
10	-6.7	-8.0	-11.1	-9.5	-8.3	-7.6	-6.3	-57.5
12	-7.2	-7.2	-5.9	-8.2	-9.4	-6.8	-5.2	-49.9

Table 6. AutoDock4.0 Docking Results

Ligand	1IYT	1J1C	1P0I	4B05	4PQE	4PWQ	503L	Total Binding Energy
1	-1.56	-7.5	-6.8	-4.47	-5.52	-5.3	-2.28	-32.83
5	-6.81	-1.89	-1.86	-6.07	-12.8	-3.96	-6.24	-39.63
6	-6.37	-5.82	-3.62	-7.13	-9.9	-5.63	-4.85	-43.32
7	-7.07	-1.43	-6.51	-7.69	-7.42	-5.06	-4.01	-39.19
8	-2.74	-6.5	-3.56	-6.14	-9.98	-3.39	-6.11	-38.42
9	-58	-1.11	-3.66	-8.03	-12.26	-4.63	-4.85	-40.34
10	-8.58	-2.38	-8.02	-9.34	-12.96	-8.73	-4.61	-54.62
12	-5.96	-1.35	-2.42	-6.23	-10.1	-3.81	-5.01	-34.89

3.4 In-Vitro anti-oxidant assay:

DPPH is a free radical in stable form which is utilized for evaluating various naturally available compounds for their anti-oxidant property. The results of our DPPH scavenging activity indicates that Oleanolic acid has potential anti-oxidant activity better than that of the positive control used (Figure 5). The antioxidant property of OA is dependent on dose (increase in dose increases activity). Similarly, the electron-donating potential can be found by Fe+3 reductions which act as the important mechanism of anti-oxidant activity.³⁵ The FRAP results showed that Oleanolic acid has higher ferric reducing power ranging from 1.153 to 1.632 when compared with control (Figure 6).

Figure 5. DPPH anti-oxidant activity results

Figure 6. FRAP anti-oxidant activity results

3. 5 In-Vitro anti-inflammatory assay:

Inflammation is the response of the immunological defence system of body against various pathogenesis including neurodegenerative diseases. Inflammation is a series of cascade reactions that is complex which results in tissue repair and damage.³⁶ Protein will lose their biological function when denatured. It is a main cause of inflammation. Compounds that inhibit protein denaturation would be considered as anti-inflammatory drug. In this investigation of Oleanolic acid, Table 8 shows that it has anti-inflammatory property by inhibiting albumin denaturation. The leukocytes proteinase has been previously reported to play a major role in the developing tissue damage. Protection from inflammation can be produced by proteinase inhibitors. Significant anti proteinase activity of OA at different concentrations is represented in Table 9. It shows significant results compared with control indicating its inflammatory inhibitory action.

Table 8. Effect of Oleanolic acid on Heat induced protein denaturation

Treatment	Concentration (µg/ml)	Absorbance at 660nm	% Inhibition of Protein denaturation
Oleanolic acid	50	0.13±0.01	65
	100	0.11±0.03	71
	200	0.10±0.03	73
Aspirin	100	0.12±0.01	68

Table 9. Effect of Oleanolic acid on protease inhibition

Treatment	Concentration (µg/ml)	Absorbance at 660nm	% Inhibition of protease action
Oleanolic acid	50	0.18±0.09	53
	100	0.15±0.04	59
	200	0.14±0.05	61
Aspirin	100	0.17±0.01	55

4.0 CONCLUSION:

To summarize the study, an attempt was initiated to identify a novel candidate from the natural resources that would elicit MTA against AD. Since AD involves various targets and till date there is no cure for it. Researchers focus mainly towards AchE and BchE considering them as novel targets despite knowing the fact that there are many more targets of AD majorly free radical outburst and inflammation. Hence, this study involves screening for MTA using Syzygium aromaticum's phytoconstituents. After performing various virtual analyses and considering the outcome from every tool utilized in this study, the phytoconstituent Oleanolic acid might be considered the potential candidate for eliciting multiple drug action against various targets of AD. The outcome of assays produced evidence that Oleanolic acid has significant anti-oxidant and inflammation inhibitory property. Furthermore, in-vivo and cell line studies can be performed for better understanding the mechanism involved by the compound in the treatment through various targets and how it affects various organs in the body.

5.0 ACKNOWLEDGEMENT:

The authors would like to acknowledge the management of Faculty of pharmacy, Sri Ramachandra Institute of Higher Education and Research for their valuable support.

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Received on 27.05.2020 Modified on 29.07.2020

Research J. Pharm. and Tech. 2021; 14(7):3515-3522.

DOI: 10.52711/0974-360X.2021.00609

Sl. No	Methods	Oral rat LD50 Predicted Values (-Log 10/ Mol)
Sl. No	Methods	1	5	6	7	8	9	10	12
1	Consensus	2.25	2.18	2.07	3.63	3.01	2.19	1.98	2.11
2	Hierarchical clustering	N/A	2.11	2.24	3.35	3.18	2.29	2.09	N/A
3	Single model	-	-	-	-	-	-	-	-
4	Group contribution	-	-	-	-	-	-	-	-
5	FDA	2.65	2.45	1.90	3.64	3.39	1.76	2.00	2.37
6	Nearest neighbour	1.84	1.89	2.07	3.89	2.47	2.52	1.87	1.84