Identification of the most potent acetylcholinesterase inhibitors from plants for possible treatment of Alzheimer’s disease: a computational approach

Abstract

Background

Being one of the rapidly growing dementia type diseases in the world, Alzheimer’s disease (AD) has gained much attention from researchers in the recent decades. Many hypotheses have been developed that describe different reasons for the development of AD. Among them, the cholinergic hypothesis depicts that the degradation of an important neurotransmitter, acetylcholine by the enzyme acetylcholinesterase (AChE), is responsible for the development of AD. Although, many anti-AChE drugs are already available in the market, their performance sometimes yields unexpected results. For this reason, research works are going on to find out potential anti-AChE agents both from natural and synthetic sources. In this study, 50 potential anti-AChE phytochemicals were analyzed using numerous tools of bioinformatics and in silico biology to find out the best possible anti-AChE agents among the selected 50 ligands through molecular docking, determination of the druglikeness properties, conducting the ADMET test, PASS and P450 site of metabolism prediction, and DFT calculations.

Result

The predictions of this study suggested that among the selected 50 ligands, bellidifolin, naringenin, apigenin, and coptisine were the 4 best compounds with quite similar and sound performance in most of the experiments.

Conclusion

In this study, bellidifolin, naringenin, apigenin, and coptisine were found to be the most effective agents for treating the AD targeting AChE. However, more in vivo and in vitro analyses are required to finally confirm the outcomes of this research.

Background

First described by Alois Alzheimer in 1907, Alzheimer’s Disease (AD) has become one of the most prevalent dementia type diseases in the world which is increasing its numbers rapidly [1, 2]. Intellectual morbidity, psychomotor dysregulation, delusions, hallucinations etc., are some of the familiar symptoms of AD [3]. In the familial and congenital cases of AD, genetic factors play critical roles [4]. Different hypotheses have been developed by the scientists that shed light on several reasons for AD onset and development. One of these hypotheses is the amyloid cascade hypothesis, which describes that the deposition of β-amyloid plaques in the brain is mainly responsible for the development of AD which is the result of abnormal processing of the amyloid precursor protein (APP) by the β-secretase enzyme. These plaques interfere with the normal functions of the brain [5]. On the other hand, another hypothesis called the oxidative stress hypothesis describes that because of the deposition of increased amounts of ions like iron, aluminium, and mercury, free radicals and reactive oxygen species (ROS) are generated very rapidly in the brain which are responsible for increased lipid peroxidation and protein and DNA oxidation. The stresses produced by these oxidation events lead the way for the AD onset [6]. There is another hypothesis of AD development which is known as the cholinergic hypothesis. According to this hypothesis, the loss of functions of the cholinergic neurons and thus the cholinergic signaling and neurotransmission in the brain maybe responsible for AD [7]. Table 1 lists the current status of therapeutic agents that are intended to or being used to alleviate the complications related to AD. This experiment was conducted focusing on the cholinergic hypothesis of AD development.

Table 1. Examples of AD treating agents, their mechanism of treatment, and their current status

Hypothesis	Treating agents	Mechanism of available treatments	Current status	Remarks	References
Amyloid cascade hypothesis	Plasma exchange with albumin 1 immunoglobulin	Exchange of plasma to remove the amyloid	Phase-III clinical trial	Ongoing research	[8, 9]
	ALZT-OP1a + ALZT-OP1b	Alleviation of amyloid plaque related neuroinflammation	Phase-III clinical trial	Ongoing research
	Elenbecestat	Removal of amyloid by inhibiting Beta-secretase 1	Phase-III clinical trial	Ongoing research
	Solanezumab	Monoclonal antibodies targeting amyloid plaque	Failed	Lack of efficacy
	Verubecestat	Removal of amyloid by inhibiting Beta-secretase 1	Failed	Lack of efficacy
	Lanabecestat	Removal of amyloid by inhibiting Beta-secretase 1	Failed	Lack of efficacy
	CAD106	Vaccine-based therapy	Phase-II clinical trial	Ongoing research
	CNP520	Inhibition of Beta-secretase 1	Phase-II clinical trial	Ongoing research
	Crenezumab	Monoclonal antibody targeting amyloid plaque	Ongoing investigation	Ongoing research
	Neflamapimod	Inhibition of inflammatory protein p38α MAPK	Ongoing investigation	Ongoing research
	Aducanumab	Monoclonal antibody targeting amyloid plaque	Phase-III clinical trial	Ongoing research
Oxidative stress hypothesis	Supplementation of Vitamin C and Vitamin E	Antioxidant to neutralize the ROS	Current investigational treatment	Ongoing study	[10, 11]
	Selenium	Antioxidant to neutralize the ROS	Ongoing investigation	Ongoing study
	Curcumin	Antioxidant to neutralize the ROS	Current investigational treatment	Ongoing study
	Ginko biloba	Antioxidant to neutralize the ROS	Current treatment	Most effective among the available treatments
	Ebenone	Antioxidant to neutralize the ROS	Ongoing investigation	Ongoing study
	Estrogen	Antioxidant to neutralize the ROS	Ongoing investigation	Ongoing study
	Colostrinin	Antioxidant to neutralize the ROS	Ongoing investigation	Ongoing study
Cholinergic hypothesis	Tacrine	Inhibition of acetylcholinesterase	Current treatment	Approved by FDA	[12]
	Donepezil	Inhibition of acetylcholinesterase	Current treatment	Approved by FDA
	Galantamine	Inhibition of acetylcholinesterase	Current treatment	Approved by FDA
	Rivastigmine	Inhibition of acetylcholinesterase	Current treatment	Approved by FDA

ROS reactive oxygen species, FDA Food and Drug Administration

The cholinergic hypothesis and the development of Alzheimer’s disease

The cholinergic hypothesis concerns with one of the major neurotransmitters, acetylcholine (ACh) which is regulated by two enzymes, acetylcholinesterase (AChE) and choline acetyltransferase (ChAT) [13]. ACh is involved in many important functions of the brain like learning and memory generation processes. It performs its functions through binding to two types of receptors, i.e., nicotinic (α7 and α4β2) and muscarinic receptors (M1 muscarinic receptor). ACh is synthesized by the enzyme choline acetyltransferase (ChAT) which catalyzes the transfer of an acetyl group from acetyl coenzyme A (Ac-CoA) to choline (Ch) in the pre-synaptic neuron and thus synthesizes the ACh. Thereafter, the ACh is secreted by the pre-synaptic neuron into the synapse where it mediates its effects by binding to either the nicotinic receptor or the muscarinic receptor. To maintain the optimal concentrations of ACh required for proper functioning of the brain, another enzyme called acetylcholinesterase (AChE) is synthesized by a serine hydrolase enzyme which hydrolyzes ACh to acetate and Ch. Then the Ch is again taken up by the pre-synaptic neuron for recycling and reusing. Thus, the balance of ACh is maintained in the normal brain. However, there is evidence that, in the brain of AD patients, the overexpression of AChE occurs. This phenomenon decreases the amount of ACh required for proper functioning of the brain which is why the neuron cells cannot operate properly and complications like brain damage as well as memory loss occur. These complications lead to the onset of AD development (Fig. 1) [14–18].

Fig. 1 [Images not available. See PDF.]

Cholinergic hypothesis and role of AChE in AD development. ACh is synthesized by ChAT and released from the pre-synaptic neuron. The ACh mediates its effects on the post-synaptic neuron through nicotinic and/or muscarinic receptors. The ACh then performs the downstream signaling in the post-synaptic neuron. AChE enzyme breaks down the ACh and overexpression of AChE lowers the amount of ACh in the brain which leads to the AD onset. AChE inhibitors repress the AChE activity, thus aid in the AD treatment

Donepezil and rivastigmine are two FDA-approved drugs that are currently used for mild to moderate AD treatment targeting the AChE enzyme. However, both of them have several side effects like nausea, diarrhea, anorexia, syncope, abdominal pain, and vomiting [19, 20]. Therefore, scientists are searching for more effective agents that can provide more efficacy than these drugs with much lesser side effects. Scientists have also focused on the natural resources for potential anti-AChE agents since the natural agents are generally much safer than synthetic chemicals. Galantamine is such a natural drug isolated from Galanthus woronowii which is also currently used for AD treatment alongside other approved chemical drugs. But since none of these drugs are found to be quite satisfactory to stop the progression or development of AD, research is going on to find out new compounds from natural sources with anti-AChE properties [21–23].

Molecular docking is a widely accepted and used technique in drug R&D which reduces both time and costs of lead discovery processes. This method is also known as computational drug design which has already been used for designing over 50 novel drugs, and many of them have also gained FDA approval for marketing. By simulating the interaction between ligand and receptor in the computer software, the docking system assigns scoring functions to the bound ligands which reflect their binding affinity. The lower docking score represents the greater binding affinity and vice-versa [24, 25]. The current study was designed to predict the best ligands among 50 selected phytochemicals with potential anti-AChE activities based on the molecular docking analysis (Table 2). Thereafter, the pharmacodynamics and physicochemical characteristics of the best selected ligands were predicted by determining their druglikeness properties, conducting the ADMET test, PASS, and P450 site of metabolism prediction and DFT calculations.

Table 2. List of the 50 anti-AChE ligands with their experimental IC50 values used in the experiment

No	Name of the compound	PubChem CID	Source plant	IC50 value	References
01	Geraniol	637566	Boesenbergia Pandurata	200±0.21 μg/ml	[26, 27]
02	Bellidifolin	5281623	Swertia chirata	18.47 ± 0.47 μg/ml	[28]
03	Mangiferin	5281647	S. chirata	4.07 ± 0.06 μg/ml	[28]
04	Allocryptopine	98570	Chelidonium majus	250 ± 2.5 μM	[29]
05	Chelidonine	197810	Chelidonium majus	26.8 ± 1.2 μM	[30]
06	Isorhamnetin	5281654	Calendula officinalis	24.18 ± 0.74 μM	[31]
07	Quercetin	5280343	Calendula officinalis	14.37 ± 0.34 μM	[30]
08	Myricetin	5281672	Scabiosa arenaria	190 ± 0.41 μg/ml	[26, 27]
09	Ostruthin	5281420	Peucedanum ostruthium	–	[32]
10	Ostruthol	6441273	Peucedanum ostruthium	–	[32]
11	Corynoline	177014	Corydalis incisa	30.6 μM/mL	[33]
12	Imperatorin	10212	Peucedanum ostruthium, Angelica dahurica	63.7 μM	[32, 33]
13	Rupicoline	633439	Tabernaemontana australis	–	[34]
14	Stylopine	6770	Corydalis crispa, Chelidonium majus	114 ± 2.9 μM	[29, 35]
15	Scoulerine	22955	Corydalis dubia	245 μM	[35]
16	Ochrobirine	629543	Corydalis crispa	–	[35]
17	Estragole	8815	O. basilicum, O. africanum, O.americanum, and O. minimum	0.337 μM	[36]
18	Naringenin	932	Citrus junos	28.2 to 134.5 μM	[37, 38]
19	Carvacrol	10364	Thymus vulgaris	0.175 mM	[39–41]
20	Myrtenal	61130	Taxus baccata	0.17 ± 0.01 mM	[41]
21	Verbenone	29025	Taxus baccata	2.66 ± 1.04 mM	[41]
22	Cyclonataminol	53316925	Buxus natalensis	23 μM	[42, 43]
23	Buxaminol A	53324855	Buxus natalensis	29.8 ± 4.4 μM	[43]
24	Skimmianine	6760	Zanthoxylum nitidum	74.09 ± 0.33 mg/mL	[44, 45]
25	Harmaline	3564	Peganum harmala	8.4 μg/mL	[46]
26	Harmine	5280953	Peganum harmala	10.9 μg/mL	[46]
27	Isoimperatorin	68081	Ruta graveolens, angelica dahurica	74.6 μM	[33, 47]
28	Xanthotoxin	4114	Ruta graveolens	5.4 × 10-5 M	[47, 48]
29	Marmesin	334704	Ruta graveolens, angelica dahurica	13.3 mM	[46, 49]
30.	1,8-cineole	2758	Inula graveolens	0.015 mg/mL	[50]
31	Eugenol	3314	Inula graveolens	0.48 mg/mL	[50]
32	α-terpineol	17100	Inula graveolens	1.3 mg/mL	[50]
33	Apigenin	5280443	Sideritis cesarea	7.72 ± 0.15 μM	[51, 52]
34	Linearol	497896	Sideritis congesta	2.66 μg/mL	[52]
35	Sidol	194142985	Sideritis congesta	0.92 μg/mL	[52]
36	Sideridiol	12315541	Sideritis congesta	8.04 μg/mL	[52]
37	Sudachitin	12443122	Micromeria cilicica	65.2 ± 0.82 μg/mL	[52]
38	Ursolic acid	64945	Nepeta sorgerae	39.19 μg/mL	[52]
39	α-pinene	6654	Salvia potentillifolia	0.022 mg/mL	[50, 52]
40	Liriodenine	10144	Beilschmiedia alloiophylla	3.5 ± 1.0 μM	[53]
41	Secoboldine	10359075	Beilschmiedia alloiophylla	10.0 ± 0.6 μM	[54]
42	Lauro-tetanine	31415	Beilschmiedia alloiophylla	3.2 ± 0.3 μM	[55]
43	Asimilobine	160875	Beilschmiedia alloiophylla	8.7 ± 1.5 μM	[53]
44	β-amyrone	12306160	Beilschmiedia alloiophylla	8.4 ± 2.0 μM	[53]
45	Berberine	2353	Coptis chinensis, Berberis bealei and Phellodendron chinense	1.48 ± 0.07 mg/ml	[54]
46	Coptisine	72322	Coptis chinensis, Berberis bealei and Phellodendron chinense	1.27 ± 0.06 mg/ml	[54]
47	Palmatine	19009	Coptis chinensis, Berberis bealei and Phellodendron chinense	5.21 ± 0.48 mg/ml	[54]
48	Luteolin	5280445	Thymus vulgaris	135 ± 0.18 μg/ml	[26, 55]
49	Rutin	5280805	Micromeria cilicica, Fraxinus angustifolia	149.0 ± 6.6 μM	[52, 56]
50	Kaempferol	5280863	Cleistocalyx operculatus	30.4 μM	[57]
Positive control 1	Donepezil	3152	NA	6.7 nM	[58]
Positive Control 2	Galantamine	9651	Galanthus woronowii	0.35 μmol/L	[59]

NA data not available

Methods

Total 50 phytochemicals were selected as ligands in this study by reviewing numerous literatures along with their IC50 values. On sequential docking experiment, four best ligands were selected as the best inhibitors of AChE. Thereafter, their different drug-like parameters were analysed in different experiments. Donepezil and galantamine were used as the positive controls in the study.

Protein preparation and ramachandran plot generation

A three-dimensional structure of AChE (PDB ID: 1ACJ) was downloaded in PDB format from protein data bank (www.rcsb.org). The proteins were then prepared and refined using the Protein Preparation Wizard in Maestro Schrödinger Suite 2018-4 [60]. During protein preparation, the bond orders were assigned and hydrogen molecules were added to heavy atoms as well as all the waters were deleted and the side chains were adjusted using Prime [61]. After that, the structure was optimized and minimized using force field OPLS_2005, which was conducted setting the maximum heavy atom RMSD (root-mean-square-deviation) to 30 Å and any remaining water less than 3 H- bonds to non-water was again deleted during the minimization step.

Ligand preparation

Three-dimensional structures of the 50 selected ligand molecules as well as the control were downloaded from PubChem database (www.pubchem.ncbi.nlm.nih.gov). These structures were prepared for docking using the LigPrep module of Maestro Schrödinger Suite [62]. Minimized 3D structures of ligands were generated by Epik2.2 using OPLS_2005 force field and within pH 7.0 ± 2.0 [63].

Receptor grid generation

Grid confines the active site of the receptor protein to a shortened specific area for the ligand to dock specifically. In glide, a grid was generated where the default Van der Waals radius scaling factor 1.0 and charge cutoff 0.25 was used. The grid was then subjected to OPLS_2005 force field. A cubic box was generated around the active site (reference ligand active site). Then, the grid box volume was adjusted to 14 × 14 × 14 for docking test.

Glide standard precision (SP) and extra precision (XP) ligand docking, Prime MM-GBSA calculation and induced fit docking

SP and XP adaptable glide dockings were conducted using the Glide module in Maestro Schrödinger Suite [64]. The Van der Waals radius scaling factor and charge cutoff were kept at 0.80 and 0.15, respectively, for all the ligand molecules. Final score was assigned by the module by analyzing the pose of docked ligand within the active site of the receptor.

After SP and XP ligand docking, the docked complexes were subjected to molecular mechanics—generalized born and surface area (MM_GBSA) rescoring with the help of Prime module of Maestro Schrödinger suite for further evaluation. This technique utilizes the docked complex and uses an implicit solvent that assigns more accurate scoring function and improves the overall free-binding affinity score upon the reprocessing of the complex. It combines OPLS molecular mechanics energies (E_MM), surface generalized born solvation model for polar solvation (G_SGB), and a nonpolar salvation term (G_NP) for total free energy (ΔG_bind) calculation. The total free energy of binding was calculated by the following equation [65]: $$\mathrm{\Delta }{\mathrm{G}}_{\text{bind}}={\mathrm{G}}_{\text{complex}}-\left({\mathrm{G}}_{\text{protein}}-{\mathrm{G}}_{\text{ligand}}\right),\text{where},\mathrm{G}={\mathrm{E}}_{\mathrm{MM}}+{\mathrm{G}}_{\mathrm{SGB}}+{\mathrm{G}}_{\mathrm{NP}}.$$

The agents with best results in the SP and XP docking were selected for the MM_GBSA and IFD studies.

Thereafter, to carry out the IFD of the selected ligand molecules from SP and XP docking, again OPLS_2005 force field was applied after generating grid around the co-crystallized ligand of the receptor, and this time, the best five ligands were docked rigidly. Receptor and ligand Van Der Waals screening was set at 0.70 and 0.50, respectively, and residues within 2 Å were refined to generate 2 best possible posses with extra precision. Four best performing ligands were selected according to their MM_GBSA score, IFD score, and XP_Gscore. The 3D representations of the best pose interactions between the best four ligands and their respective receptors were obtained using Discovery Studio Visualizer [66]. At these stages, the docking parameters of the compounds under investigation were compared with the control

Ligand-based drug likeness property and ADMET prediction

The drug likeness properties of the 4 selected ligand molecules were analyzed using SWISSADME server (http://www.swissadme.ch/) [67]. After that, the ADMET for each of the ligand molecules was conducted using the online based server, ADMETlab (http://admet.scbdd.com/) to predict various pharmacokinetic and pharmacodynamic properties [68, 69]. The numeric and categorical values of the results generated by the ADMETlab server were converted into qualitative values according to the documentation and explanation described in the ADMETlab server (http://admet.scbdd.com/home/interpretation/) for the convenience of interpretation.

PASS and SOM prediction

The PASS (Prediction of Activity Spectra for Substances) prediction of the best four selected ligands were carried out by the PASS-Way2Drug server (http://www.pharmaexpert.ru/passonline/), using the canonical SMILES from PubChem server (https://pubchem.ncbi.nlm.nih.gov/) [70]. While carrying out PASS prediction, the P_a (probability to be active) was kept greater than 70%, since the P_a > 70% threshold generates highly reliable prediction [71]. In the PASS prediction study, 15 possible biological activities were predicted. The P450 Site of Metabolism (SOM) of the four best selected ligand molecules was determined by online tool, RS-WebPredictor 1.0 (http://reccr.chem.rpi.edu/Software/RS-WebPredictor/) [72]. Moreover, the LD50 value and toxicity class of the compounds were predicted using the ProTox-II server (http://tox.charite.de/protox_II/) [73].

DFT calculations

Minimized ligand structures obtained from LigPrep were used for DFT calculation using the Jaguar panel of Maestro Schrödinger Suite using Becke’s three-parameter exchange potential and Lee-Yang-Parr correlation functional (B3LYP) theory with 6-31G* basis set [74–76]. Quantum chemical properties such as surface properties (MO, density, potential) and multipole moments were calculated along with HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) energy. Then, the global frontier orbital was analyzed and hardness (η) and softness (S) of selected molecules were calculated using the following equation as per Parr and Pearson interpretation and Koopmans theorem [77, 78]. The DFT calculation was done for the 3 best ligand molecules. $$\begin{array}{c}\eta =\left(\text{HOMOε}-\text{LUMOε}\right)/2,\\ S=1/\eta \end{array}$$

Result

Molecular docking study

All the 50 selected ligands were docked successfully with their receptor protein, AchE. The ligand molecules that had the lowest binding energy were considered the best ligand molecules because lower binding energy or docking score represents higher binding affinity [79]. In the MM-GBSA study, the most negative ΔGBind score is also considered as the best ΔGBind score [80]. IFD study was conducted to determine the accurate binding mode and accuracy of active site geometry. The lowest values of IFD score and XP GScore represent the best results [81–84]. From the initial 50 ligands, the ligands that had both SP and XP scores of less than − 8.00 Kcal/mol were selected for MM_GBSA analysis and IFD study. Thirteen ligands were found to have both SP and XP scores less than − 8.00 Kcal/mol: bellidifolin, isoharmnetin, quercetin, myricetin, imperatorin, naringenin, harmaline, harmine, isoimperatorin, apigenin, coptisine, liriodenine, and scoulerine (Table 3). From these 13 ligands, 4 ligands were finally selected based on their lower scores in both MM_GBSA (∆G_bind score) and IFD studies (XP G_score and IFD score), i.e., bellidifolin, naringenin, apigenin, and coptisine (Table 4). These four ligands were selected for further analysis to predict and determine their drug potentials. When compared to the two positive controls: donepezil and galantamine, it can be declared that these four compounds generated sound results in the molecular docking study.

Table 3. The results of the SP and XP docking study of the selected ligands along with their respective glide energies

No	Name of the compound	Standard Precision (SP) Docking score/ Binding energy (Kcal/mol)	Extra Precision (XP) Docking score/ Binding energy (Kcal/mol)	Glide energy (Kcal/mol)
01	Coptisine	− 11.12	− 15.98	− 48.31
02	Myricetin	− 10.37	− 12.43	− 42.546
03	Quercetin	− 9.82	− 11.22	− 44.872
04	Isorhamnetin	− 9.66	− 10.31	− 47.76
05	Apigenin	− 9.51	− 9.96	− 44.67
06	Naringenin	− 9.36	− 10.22	− 53.39
07	Bellidifolin	− 8.96	− 9.79	− 49.191
08	Harmine	− 8.73	− 8.97	− 49.149
09	Imperatorin	− 8.49	− 8.81	− 42.072
10	Harmaline	− 8.34	− 9.07	− 38.62
11	Scoulerine	− 8.33	− 8.14	− 41.25
12	Marmesin	− 8.10	− 7.73	− 23.41
13	Isoimperatorin	− 8.06	− 7.46	− 33.972
14	Liriodenine	− 8.01	− 7.31	− 45.69
15	Carvacrol	− 7.99	− 7.19	− 27.98
16	Xanthotoxin	− 7.84	− 7.78	− 39.22
17	Corynoline	− 7.69	− 7.11	− 39.95
18	Ostruthin	− 7.59	− 6.90	− 48.51
19	β-amyrone	− 7.41	− 7.63	− 40.65
20	Lauro-tetanine	− 7.31	− 6.33	− 31.50
21	Ostruthol	− 7.28	− 9.52	− 42.707
22	Sidol	− 7.23	− 6.65	− 36.53
23	Berberine	− 7.13	− 6.63	− 47.86
24	Buxaminol A	− 6.84	− 7.44	− 24.70
25	Stylopine	− 6.74	− 6.12	− 32.544
26	Ochrobirine	− 6.74	− 7.87	− 28.776
27	Secoboldine	− 6.71	− 8.48	− 33.87
28	Skimmianine	− 6.62	− 6.13	− 40.65
29	Palmatine	− 6.45	− 6.90	− 30.91
30	α-pinene	− 6.33	− 8.09	− 30.32
31	Sudachitin	− 6.31	− 8.26	− 21.28
32	Eugenol	− 6.30	− 6.42	− 24.55
33	Kaempferol	− 6.27	− 9.25	− 31.34
34	Myrtenal	− 6.15	− 7.95	− 43.53
35	Cyclonataminol	− 6.11	− 5.41	− 33.46
36	Rupicoline	− 5.99	− 7.03	− 48.430
37	α-terpineol	− 5.84	− 6.32	− 36.76
38	Estragole	− 5.77	− 6.24	− 44.384
39	Luteolin	− 5.74	− 6.87	− 28.76
40	Geraniol	− 5.69	− 7.19	− 34.712
41	1,8-cineole	− 5.67	− 4.05	− 44.68
42	Asimilobine	− 5.66	− 7.05	− 22.69
43	Rutin	− 5.64	− 7.30	− 43.66
44	Sideridiol	− 5.54	− 8.94	− 28.92
45	Ursolic acid	− 5.40	− 6.95	− 34.12
46	Linearol	− 5.24	− 6.56	− 36.59
47	Chelidonine	− 5.15	− 5.89	− 40.963
48	Verbenone	− 4.80	− 4.17	− 27.52
49	Mangiferin	− 4.65	− 4.22	− 29.058
50	Allocryptopine	− 4.39	− 2.66	− 32.638
Positive control 1	Donepezil	− 7.34	− 9.10	− 43.91
Positive Control 2	Galantamine	− 6.15	− 8.21	− 41.29

Table 4. The results of the MM_GBSA and IFD studies of the 13 ligands selected from the SP and XP docking studies along with the positive controls

No	Name	MM_GBSA ∆Gbind score (Kcal/mol)	XP Gscore (Kcal/mol)	IFD score (Kcal/mol)
01	Coptisine	− 59.41	− 16.24	− 1160.39
02	Bellidifolin	− 55.23	− 10.58	− 1155.67
03	Apigenin	− 49.07	− 11.78	− 1154.41
04	Narigenin	− 42.02	− 12.24	− 1156.52
05	Harmine	− 41.20	− 10.24	− 1153.45
06	Imperatorin	− 38.81	− 8.70	− 1152.22
07	Myricetin	− 36.53	− 11.72	− 1149.29
08	Isoharmnetin	− 35.29	− 9.69	− 1153.95
09	Scoulerine	− 33.43	− 9.22	− 1140.92
10	Harmaline	− 32.24	− 6.78	− 1159.54
11	Isoimperatorin	− 32.96	− 7.54	− 1155.66
12	Quercetin	− 30.54	− 8.01	− 1151.03
13	Liriodenine	− 29.19	− 7.80	− 1152.97
Positive control 1	Donepezil	− 47.06	− 8.21	− 1148.97
Positive Control 2	Galantamine	− 40.92	− 7.40	− 1148.23

Binding mode of best ligands with respective targets

The 3D representations as well as the interaction of different amino acids of the binding pocket of AChE with bellidifolin, naringenin, apigenin, and coptisine are illustrated in Fig. 2 and listed in Supplementary Table S1.

Fig. 2 [Images not available. See PDF.]

Figure showing the various types of bonds and amino acids that take part in the interaction between the 4 best selected ligands (a bellidifolin, b naringenin, c apigenin, d coptisine) and their receptor, AChE. Interacting amino acid residues of target molecule are labeled in the diagram and dotted lines depict the interaction between the ligand and receptor. Green dotted lines—conventional bond; deep pink—Pi-Pi stacked bond; and orange—charge-charge interaction

Bellidifolin generated an MM_GBSA score of − 55.23 Kcal/mol when docked with acetylcholinesterase. It also generated the IFD score of − 1155.67 Kcal/mol and XP G_score of − 10.58 Kcal/mol against acetylcholinesterase. It formed two hydrogen bonds with glutamic acid 199 and histidine 440 at 2.18 Å and 2.44 Å distance apart, respectively. It was also reported to form multiple pi-pi stacked hydrophobic interactions with phenylalanine 330 (×2) and tryptophan 84 (×6) amino acid residues within the binding pocket of AChE.

Naringenin was docked with acetylcholinesterase where it generated the MM_GBSA score of − 42.02 Kcal/mol, IFD score of − 1156.52 Kcal/mol, and XP G_score of − 12.24 Kcal/mol. It formed four hydrogen bonds with histidine 440 (×2), tyrosine 70, and proline 86 at 1.69 Å, 2.38 Å, 1.66 Å, and 2.99 Å distance apart, respectively. It was also found to form multiple pi-pi stacked hydrophobic interactions with phenylalanine 330 and tryptophan 84 (×2) amino acid residues within the binding pocket of AChE.

Apigenin was docked with acetylcholinesterase where it generated the MM_GBSA score of − 49.07 Kcal/mol, IFD score of − 1154.41 Kcal/mol, and XP G_score of − 11.78 Kcal/mol. It formed six hydrogen bonds with glutamic acid 199 (×2), glycine 117, glycine 123, serine 122, and tyrosine 130 at 2.55 Å, 2.26 Å, 2.77 Å, 2.90 Å, 2.73 Å, and 2.76 Å distance apart, respectively. It was also formed multiple pi-pi stacked hydrophobic interactions with tryptophan 84 (×5), histidine 440, and phenylalanine 330 (×2) amino acid residues within the binding pocket of AChE.

Coptisine was docked with acetylcholinesterase, and it was found to generate the MM_GBSA score of − 59.41 Kcal/mol, IFD score of − 1160.39 Kcal/mol, and XP G_score of − 16.24 Kcal/mol. It formed six hydrogen bonds with histidine 440 (×2), serine 122, and glycine 117 at 2.21 Å, 2.13 Å, 3.01 Å, 2.90 Å, and 2.53 Å distance apart, respectively. It was also predicted to form multiple hydrophobic interactions with tryptophan 84 (×7), tryptophan 432, and phenylalanine 330 (×3) amino acid residues within the binding pocket of AChE.

Druglikeness properties

The druglikeness property experiment was conducted for only the 4 best selected ligands: bellidifolin, naringenin, apigenin, and coptisine. All the 4 ligands were predicted to follow the Lipinski’s rule of five. Moreover, bellidifolin was found to have the highest topological polar surface area (TPSA) value of 100.13 Ų. Again, coptisine was predicted to have the highest molar refractivity of 87.95 as well as the highest concensus Log P_o/w of 2.40 (Table 5). All the found compounds generated quite sound performance in the analysis when compared with the two positive controls, donepezil and galantamine.

Table 5. The druglikeness properties of the best 4 ligands and the positive controls

Drug Likeness Properties	Rules	Bellidifolin	Naringenin	Apigenin	Coptisine	Donepezil (positive control 1)	Galantamine (positive control 2)
Lipinski’s rule of five	–	Yes	Yes	Yes	Yes	Yes	Yes
Molecular weight	< 500	274.23 g/mol	272.25 g/mol	270.24 g/mol	320.32 g/mol	379.49 g/mol	287.35 g/mol
Concensus log Po/w	≤ 5	1.77	1.84	2.11	2.40	4.00	1.91
Num. H-bond acceptors	< 10	6	5	5	4	4	4
Num. H-bond donors	< 5	3	3	3	0	0	1
Molar refractivity	40–130	72.55	71.57	73.99	87.95	115.31	84.05
No of rotatable bonds	≤ 10	1	1	1	0	6	1
TPSA (Å2)	–	100.13 Å2	71.57	90.90 Å2	40.80 Å2	38.77	41.93

ADMET prediction

The results of the ADMET test are summarized in Table 6 along with their comparison with donepezil and galantamine. All the four ligands performed quite similarly in the ADMET test. In the absorption section, all the ligands were predicted to be optimal Caco-2 permeable, whereas all of them were P-gp (P-glycoprotein) non-inhibitors and P-gp non-substrates. In the distribution section, apigenin showed relatively poor performance with no capacity to be blood-brain barrier (BBB) permeable. In the metabolism section, all of the ligands were predicted to be CYP450 1A2 inhibitor and only apigenin was found to be CYP450 1A2 non-substrate. However, all of them were CYP450 2C9 and CYP450 2C19 non-inhibitors and bellidifolin and apigenin were substrates for CYP450 2C9. Moreover, only bellidifolin and coptisine were found to be substrates for CYP450 2C19 and CYP450 2D6. In the excretion section, coptisine was found to have the highest half-life of 1.8 h. In the toxicity section, all the four ligands were found to be drug induced liver toxicity (DILI) positive and only bellidifolin was Ames positive as well as hERG non-blocker. Moreover, both naringenin and coptisine were found to have human hepatotoxicity. The performance of the 4 finally selected ligands was not very different from that of the two positive controls.

Table 6. The ADME/T test results of the best 4 ligand molecules along with the two positive controls

Class	Properties	Bellidifolin	Naringenin	Apigenin	Coptisine	Donepezil (positive control 1)	Galantamine (positive control 2)
Absorption	Caco-2 permeability	Optimal	Optimal	Optimal	Optimal	Optimal	Optimal
	Pgp-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Inhibitor	Non-inhibitor
	Pgp-substrate	Non-substrate	Non-substrate	Non-substrate	Non-substrate	Non-substrate	Substrate
	Human Intestinal Absorption (HIA)	HIA positive	HIA positive	HIA positive	HIA negative	HIA positive	HIA positive
Distribution	Plasma Protein Binding	Moderate	Moderate	Optimal	Low	Optimal	Low
	BBB (Blood–Brain Barrier)	BBB positive	BBB positive	BBB negative	BBB positive	BBB positive	BBB positive
Metabolism	CYP450 1A2 inhibitor	Inhibitor	Inhibitor	Inhibitor	Inhibitor	Non-inhibitor	Non-inhibitor
	CYP450 1A2 substrate	Substrate	Substrate	Non-substrate	Substrate	Non-substrate	Substrate
	CYP450 3A4 inhibitor	Non-inhibitor	Inhibitor	Inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor
	CYP450 3A4 substrate	Non-substrate	Non-substrate	Non-substrate	Substrate	Substrate	Substrate
	CYP450 2C9 inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor
	CYP450 2C9 substrate	Substrate	Non-substrate	Substrate	Non-substrate	Non-substrate	Non-substrate
	CYP450 2C19 inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor	Non-inhibitor
	CYP450 2C19 substrate	Substrate	Non-substrate	Non-substrate	Substrate	Substrate	Non-substrate
	CYP450 2D6 inhibitor	Non-inhibitor	Non-inhibitor	Inhibitor	Inhibitor	Inhibitor	Inhibitor
	CYP450 2D6 substrate	Substrate	Non-substrate	Non-substrate	Substrate	Substrate	Substrate
Excretion	T1/2 (h)	1.0	0.9	1.3	1.8	1.6	1.7
Toxicity	hERG (hERG Blockers)	Non-blocker	Blocker	Blocker	Blocker	hERG positive	Non-blocker
	H-HT (Human Hepatotoxicity)	HHT positive	HHT negative	HHT positive	HHT negative	HHT positive	HHT positive
	Ames (Ames Mutagenicity)	Ames positive	Ames negative	Ames negative	Ames negative	Ames negative	Ames negative
	DILI (Drug Induced Liver Injury)	DILI positive	DILI positive	DILI positive	DILI positive	DILI negative	DILI negative

PASS and P450 site of metabolism prediction

The predicted LD50 value for bellidifolin, naringenin, apigenin, and coptisine were 4000 mg/kg, 2000 mg/kg, 2500 mg/kg, and 1000 mg/kg, respectively. Moreover, both bellidifolin and apigenin were predicted to be in toxicity class 5, whereas naringenin and coptisine were found to be in toxicity class 4. The prediction of activity spectra for substances (PASS prediction) was conducted for the 4 ligands to identify 15 intended biological activities. The PASS prediction results of the 4 selected ligands along with the positive control donepezil are listed in Table 7. Coptisine did not show any of the activities; however, both bellidifolin and apigenin were predicted to have 14 activities each. Naringenin showed all the 15 intended biological activities. On the other hand, the possible sites of metabolism (SOMs) by CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4 of bellidifolin, naringenin, apigenin, and coptisine were also determined, and the possible SOMs by these isoforms are indicated by circles on the chemical structure of the molecule [72] (Table 8). In both PASS and P450 SOM prediction, all the 4 compounds showed quite satisfactory performance which is comparable to the positive controls, donepezil and galantamine.

Table 7. The PASS prediction results showing the biological activities of the best 4 ligands and the positive controls

Sl no	Biological activities	Bellidifolin		Naringenin		Apigenin		Coptisine		Donepezil (positive control 1)		Galantamine (positive control 2)
		Predicted LD50: 4000 mg/kg		Predicted LD50: 2000 mg/kg		Predicted LD50: 2500 mg/kg		Predicted LD50: 1000 mg/kg		Predicted LD50: 1500 mg/kg		Predicted LD50: 2000 mg/kg
		Toxicity class: 5		Toxicity class: 4		Toxicity class: 5		Toxicity class: 4		Toxicity class: 4		Toxicity class: 4
		Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi
01	Antioxidant	–	–	0.794	0.003	0,732	0,004	–	–	0.893	0.003	0.781	0.010
02	Membrane integrity agonist	0.956	0.003	0.964	0.003	0.967	0.002	–	–	0.768	0.017	0.813	0.005
03	Antineoplastic	0.742	0.019	0.751	0.018	0.774	0.015	–	–	0.823	0.005	0.956	0.002
04	Antimutagenic	0.891	0.002	0.857	0.003	0.921	0.002	–	–	0.799	0.009	0.942	0.002
05	Mucomembranous protector	0.710	0.052	0.844	0.010	0.797	0.019	–	–	0.773	0.011	0.764	0.014
06	Membrane integrity agonist	0.754	0.010	0.924	0.003	0.967	0.002	–	–	0.834	0.004	0.720	0.015
07	TP53 expression enhancer	0.853	0.007	0.822	0.009	0.873	0.006	–	–	0.920	0.002	0.798	0.008
08	Lipid peroxidase inhibitor	0.755	0.004	0.815	0.003	-	-	–	–	0.875	0.009	0.909	0.005
09	HIF1A expression inhibitor	0.937	0.004	0.911	0.005	0.911	0.005	–	–	0.751	0.019	0.866	0.009
10	APOA1 expression enhancer	0.702	0.005	0.836	0.003	0.826	0.003	–	–	0.918	0.002	0.978	0.001
11	Free radical scavenger	0.719	0.004	0.769	0.003	0.719	0.004	–	–	0.911	0.005	0.782	0.010
12	Peroxidase inhibitor	0.908	0.003	0.818	0.004	0.924	0.002	–	–	0.912	0.008	0.930	0.003
13	Vasoprotector	0.758	0.007	0.707	0.010	0.891	0.003	–	–	0.711	0.022	0.748	0.013
14	Histidine kinase inhibitor	0.889	0.002	0.892	0.002	0.918	0.002	–	–	0.931	0.003	0.814	0.011
15	Apoptosis agonist	0.833	0.006	0.709	0.014	0.847	0.005		–	0.891	0.006	0.951	0.001

Table 8. The P450 site of metabolism prediction of the best four ligand molecules and the positive controls

Analysis of frontier’s orbitals

In the analysis of Frontier’s orbitals, the DFT calculations were performed. The results of the DFT calculations are listed in Table 9. Coptisine showed the lowest gap energy of 0.047 eV as well as the highest dipole moment of 6.459 debye. On the other hand, apigenin generated the highest gap energy of 0.130 eV and the second highest dipole moment of 2.163 debye. The order of dipole moments of these 4 compounds were, bellidifolin < apigenin < naringenin < coptisine. Comparing with the positive controls, it can be declared that all the four compounds showed sound results in the DFT calculations (Fig. 3).

Table 9. The results of the DFT calculations of the selected best 4 ligands and the positive controls

Compound name	HOMO energy (eV)	LUMO energy (eV)	Gap (eV)	Hardness (η) (eV)	Softness (S) (eV)	Dipole moment (Debye)
Bellidifolin	− 0.015	0.101	0.116	0.058	17.241	1.630
Naringenin	− 0.005	0.094	0.099	0.049	20.408	2.278
Apigenin	− 0.048	0.082	0.130	0.065	15.385	2.163
Coptisine	− 0.134	− 0.087	0.047	0.023	21.276	6.459
Donepezil (positive control 1)	− 0.028	0.076	0.104	0.032	18.560	2.398
Galantamine (positive control 2)	− 0.022	0.081	0.103	0.047	16.141	3.041

Fig. 3 [Images not available. See PDF.]

The results of HOMO and LUMO studies; a bellidifolin, b naringenin, c apigenin, and d coptisine

Discussion

Molecular docking is an effective strategy in computer-aided drug designing which works on some specific algorithms and assigns affinity scores depending on the poses of the ligands inside the binding pocket of a target. In molecular docking, the lowest docking score corresponds to the highest affinity which reflects that the complex remains more time in contact with good stability [85, 86].

In this study, 50 compounds that were proven to have anti-AChE properties were selected by literature reviewing. Initially, from these 50 compounds, 13 compounds were selected based on their SP and XP docking scores (compounds having both SP and XP docking scores over − 8.0 Kcal/mol). When compared with the positive controls, donepezil and galantamine, all the compounds were found to have relatively good results in molecular docking analysis. The 13 ligands selected from the initial docking analysis had predicted results better than donepezil. Later, from these 13 compounds, according to the MM_GBSA and IFD scores, 4 compounds, i.e., bellidifolin, naringenin, apigenin, and coptisine, were selected as final ligands for further analysis. These final compounds were also predicted to have better results than donepezil and galantamine in the MM_GBSA and IFD studies.

AChE possesses several notable amino acids in its active-site gorge, i.e., serine 200, glutamic acid 327, histidine 440, tryptophan 279, tyrosine 121, phenylalanine 330, and tryptophan 84. All the 4 finally selected ligands were found to form potential hydrogen and hydrophobic interactions with histidine 440, phenylalanine 330, and tryptophan 84 within the active site of AChE. Since hydrogen and hydrophobic interactions are important for strengthening the receptor-ligand interactions, it can be declared that these ligands might be able to effectively inhibit the AChE enzyme at its active site [87, 88].

The prediction of druglikeness properties facilitates the drug development processes. The molecular weight and TPSA are two important characteristics of a drug that affect its permeability through the biological barriers. Higher molecular weight and TPSA represent lower permeability of the drug molecule and vice-versa. Again, the lipophilicity (expressed as LogP) influences the absorption of the drug molecule in the body and the higher LogP of a drug represents its lower absorption in the body. The capability of a drug molecule to cross the cell membrane is also influenced by the number of hydrogen bond acceptors and donors beyond the acceptable range. Moreover, the number of rotatable bonds (acceptable range is less than 10) also influences the druglikeness properties of a drug. Furthermore, the Lipinski’s rule of five demonstrates that a successful drug should have properties within the acceptable range of the five Lipinski’s rules, i.e., molecular weight ≤ 500, number of hydrogen bond acceptors ≤ 10, number of hydrogen bond donors ≤ 5, molar refractivity from 40 to 130, and lipophilicity (expressed as LogP) ≤ 5 [89–92]. All the 4 selected ligands were found to follow the five Lipinski’s rules of druglikeness properties. All of them showed quite good results in the druglikeness property experiment and for this reason, and all of them were considered as potential drug candidates.

ADMET prediction of a candidate drug aids in determining the pharmacological and pharmacodynamic properties of the drug molecule within a biological system. For this reason, the ADMET properties are important determinants for the success of a drug development process. BBB permeability is the most important characteristic of the drugs which primarily target the brain cells. Again, the transportation of drugs is influenced by the P-glycoprotein (P-gp) in the cell membrane; therefore, inhibition of this protein severely affects the drug transport. Moreover, if a drug is found to be permeable in the Caco-2 cell line, then that particular drug is considered to be absorbed well in the intestine. Orally absorbed drugs travel through the blood circulation and deposit in the liver where they are metabolized by group of enzymes of the Cytochrome P450 family and later excreted as bile or urine. Therefore, inhibition of any of these enzymes affects the metabolism and biodegradation of the drugs [93, 94]. Furthermore, if a drug is found to be a substrate for one or more CYP450 enzyme or enzymes, then that drug is considered to be metabolized well by the respective CYP450 enzyme or enzymes [95]. A drug’s pharmacodynamic properties depend on the degree of its binding with the plasma protein. A drug can pass through the cell layers if it binds to the plasma proteins less efficiently and vice versa [96]. Human intestinal absorption (HIA) is another crucial determinant for the orally administered drugs [97–99]. The half-life of a drug describes that the greater the half-life, the longer it would stay in the body and the greater its potentiality [100–102].

Furthermore, HERG is a K+ channel which is found in the heart muscle. Blocking the hERG channel or signaling may cause cardiac arrhythmia leading to death [103, 104]. On the other hand, human hepatotoxicity (H-HT) refers to any type of acute liver injury which may lead to organ failure and death [105, 106]. The Ames test is a mutagenicity assay that is used to detect the potential mutagenic chemicals [107]. Drug induced liver injury (DILI) reflects the injuries in the liver that are caused by administration of drugs [108]. Bellidifolin, naringenin, and coptisine showed quite similar and sound performance in the AMDET prediction. However, apigenin was found to be BBB impermeable and also a good binder to the plasma membrane, which could affect its biodistribution throughout the body as a potential inhibitor of AChE. As a consequence, the performance of apigenin in ADMET test was declared to be non-satisfactory.

Prediction of Activity Spectra for Substances or PASS prediction is a process of determining the biological activities of drug candidates or drug-like molecules. The PASS prediction estimates biological activities by predicting two potential probabilities: Pa, which represents the probability of a compound to be active and Pi, which represents the probability of a compound to be inactive. By predicting these two probabilities, the PASS prediction determines potential biological activities of a drug-like molecule. The Pa value of greater than 0.7 represents the greater probability of exhibiting the activity of a substance in an experiment. The Pa > 70% threshold corresponds to a highly reliable prediction, while Pa > 0.5 gives quite reliable prediction and the Pa > 0.3 threshold describes that a compound is less likely to have the desired activity [70, 71, 109]. Most of the intended activities (13 activities) were common between bellidifolin, naringenin, and apigenin, for example, antineoplastic, antimutagenic, membrane integrity agonist, HIF1A expression inhibitor, and apoptosis agonist (Table 8).

The toxicity classes of the 4 compounds were determined by the ProTox-II server (http://tox.charite.de/protox_II/index.php?site=compound_input), which predicts the toxicity of a compound and then classifies the compound into a toxicity class that ranges from 1 to 6. To classify the compounds into toxicity classes, the server uses the Globally Harmonized System of Classification and Labelling of Chemicals (GHS). According this system, since both bellidifolin and apigenin had toxicity class 4, they would be harmful if swallowed. Again with the toxicity class of 5, both naringenin and coptisine might be harmful if swallowed [73, 110]. Both the positive controls, donepezil and galantamine were found to have the toxicity class of 4.

The cytochrome P450 or CYP450 represents a family of 57 isoforms of the heme-containing P450 enzymes. This enzyme family is one of the most important enzyme family for proper metabolism within the human body, which is responsible for catalyzing the phase-I metabolism of about 90% of all the marketed drugs. They also convert the lipophilic drugs or compounds to more polar, hydrophilic substances. Among these 57 isoforms of the CYP450 enzymes, 9 isoforms are most important for biological activities, i.e., CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4 [111, 112]. When the P450 SOMs of the 4 best selected ligands: bellidifolin, naringenin, apigenin, and coptisine were predicted, all of them showed potential SOMs where these 9 isoforms of P450 enzymes can act on. For this reason, all these ligands or compounds might be metabolized well by the body.

Frontier’s orbitals study or DFT calculation is an important method to predict the pharmacological properties of small molecules. HOMO and LUMO help to study the chemical reactivity and kinetic stability of the query molecules. The term “HOMO” represents the regions on a small molecule which might donate electrons during the formation of a complex and the term “LUMO” corresponds to the regions on a molecule that might receive electrons from the electron donor. The difference in HOMO and LUMO energy is regarded as gap energy that corresponds to the electronic excitation energy. The molecule that has greater orbital gap energy tends to be energetically more unfavorable to undergo a chemical reaction and vice versa [76, 113–117]. All of the 4 ligands were predicted to have significant gap energy representing their possibility to undergo a chemical reaction. However, among the four best selected ligands, coptisine, with its lowest gap energy, was predicted to have higher probability to undergo a chemical reaction [25, 117] (Table 9 and Fig. 3).

Considering all the aspects of the study and comparing the four best selected compounds, it can be concluded that bellidifolin has the highest potentiality to be used as anti-AD drug. From Table 10, it is clear that bellidifolin generated the most sound and satisfactory performance among the four compounds, with no poor performance in any of the analysis. Even compared to the positive controls, donepezil and galantamine, bellidifolin generated very good results in almost all the analyses. Therefore, bellidifolin can be considered as the most suitable compound as a drug for AD treatment among the compounds considered in this study. However, further investigation could also be conducted on other compounds as they had also shown convincing results in the docking analysis (Table 3). And the authors recommend further in vivo and in vitro experiments to strengthen the findings of this study.

Table 10. Overall comparison of the main features of bellidifolin, naringenin, apigenin, and coptisine as potential drugs to treat AD according to the study along with the 2 positive controls from comparison. All the compounds are ranked (excellent, good, and poor) based on their performance in every aspect

Features	Bellidifolin	Naringenin	Apigenin	Coptisine	Donepezil (positive control 1)	Galantamine (positive control 2)	Remarks (Best performers)
Molecular docking study	Good	Good	Good	Excellent	Good	Poor	Coptisine
Druglikeness properties	Excellent	Excellent	Excellent	Excellent	Excellent	Excellent	All
Absorption	Excellent	Excellent	Excellent	Poor	Good	Poor	Bellidifolin, naringenin, apigenin
Distribution	Excellent	Excellent	Poor	Poor	Excellent	Good	Bellidifolin, naringenin
Metabolism	Excellent	Poor	Good	Good	Good	Excellent	Bellidifolin
Excretion	Good	Good	Good	Excellent	Excellent	Excellent	Coptisine
Toxicity	Good	Excellent	Good	Excellent	Excellent	Excellent	Naringenin & apigenin
PASS Prediction	Good	Good	Good	Good	Good	Good	All
P450 SOM	Good	Excellent	Excellent	Poor	Good	Good	Naringenin, apigenin
DFT calculations	Good	Good	Poor	Excellent	Good	Good	Coptisine

Conclusion

As Alzheimer’s disease (AD) is one of the most rapidly growing dementia type diseases in the world, more and more research studies are being conducted to find out the best possible treatment for this disease. Although several treatments are already available, none of these treatments possesses significant satisfactory results. Plants have been known to contain many beneficial agents that can be used to treat a variety of diseases. In this experiment, 50 anti-AChE phytochemicals were selected to analyze their activities against the acetylcholinesterase (AChE) enzyme, which is a key enzyme responsible for the development of AD, by exploiting numerous approaches used in computer-aided drug design. Upon continuous computational experimentation, bellidifolin, naringenin, apigenin, and coptisine were predicted to be the best inhibitors of AChE. Then, their drug potentiality was checked in different post-screening studies where they were also predicted to show quite similar and sound performance, although in some aspects, their performances were not up to the mark. However, more wet-lab based studies must be performed on these 4 agents as well as the other remaining agents to finally confirm their potentiality, safety, and efficacy.

Acknowledgements

The authors are thankful to the Swift Integrity Computational Lab, Dhaka, Bangladesh, a virtual platform of young researchers, for providing the support.

Authors’ contributions

BS supervised and designed the study; SA conducted the molecular docking study; TKR performed the druglikeness properties and ADMET test; SSI performed the PASS prediction and DFT calculations; YA wrote the manuscript; MAU edited the manuscript. The authors have read and approved the manuscript.

Funding

No funding was received for conducting the study.

Availability of data and materials

The tools used in this study are available on the Internet.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest among themselves.

Abbreviations

AD

Alzheimer’s disease

APP

Amyloid precursor protein

AChE

Acetylcholinesterase

ChAT

Choline acetyltransferase

Ac-CoA

Acetyl coenzyme A

Ch

Choline

PASS

Prediction of Activity Spectra for Substances

SOM

Site of metabolism

MM-GBSA

Molecular mechanics – generalized born and surface area

HOMO

Highest occupied molecular orbital

LUMO

Lowest unoccupied molecular orbital

TPSA

Topological polar surface area

BBB

Blood-brain barrier

HERG

Human ether-a-go-go-related gene

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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