Key features for designing phosphodiesterase-5 inhibitors

Phosphodiesterase superfamily is the key regulator of 3',5'-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q(2), and non cross-validated coefficient, r(2), values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate. Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

Structure-based and ligand-based drug design for microsomal prostaglandin E synthase-1 inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) has been regarded as an attractive drug for inflammation-related diseases. In search of new mPGES-1 inhibitors, we performed virtual screening using our traditional Chinese medicine and natural products database (http://tcm.cmu.edu.tw/) and constructed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) using a training set of 30 experimentally tested mPGES-1 inhibitors. The CoMFA and CoMSIA models derived were statistically significant with cross-validated coefficient values of 0.808 for CoMFA and 0.829 for CoMSIA and non-cross-validated coefficient values of 0.829 for CoMFA and 0.980 for CoMSIA. Docking and de novo evolution design gave three top derivatives, 2-O-caffeoyl tartaric acid-Evo_2, glucogallin-Evo_1 and 3-O-feruloylquinic acid-Evo_7 that have higher binding affinities than the control, glutathione. These three derivatives have interactions with Arg70, Arg73, Arg110, Arg126 and Arg38, which all are mPGES-1 key active site residues. In addition, these derivatives fit well into the CoMFA and CoMSIA models, with hydrophobic, hydrophilic and electropositive substructures mapped onto corresponding contour plots. Hence, we suggest that these three de novo compounds could be a starting basis for new mPGES-1 inhibitors.     

Identification of potent EGFR inhibitors from TCM Database@Taiwan

Overexpression of epidermal growth factor receptor (EGFR) has been associated with cancer. Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells. Hence, we employed Traditional Chinese Medicine Database (TCM nawiaT@esabataD) (http://tcm.cmu.edu.tw) to identify potential EGFR inhibitor. Multiple Linear Regression (MLR), Support Vector Machine (SVM), Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarities Indices Analysis (CoMSIA) models were generated using a training set of EGFR ligands of known inhibitory activities. The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid, and all had higher binding affinities than the control Iressa®. The TCM candidates had interactions with Asp855, Lys716, and Lys728, all which are residues of the protein kinase binding site. Validated MLR (r² = 0.7858) and SVM (r² = 0.8754) models predicted good bioactivity for the TCM candidates. In addition, the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship (3D-QSAR) map derived from the CoMFA (q² = 0.721, r² = 0.986) and CoMSIA (q² = 0.662, r² = 0.988) models. The steric field, hydrophobic field, and H-bond of the 3D-QSAR map were well matched by each TCM candidate. Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain. Based on the different structures, H-bonds were formed at either Asp855 or Lys716/Lys728. The compounds remained stable throughout molecular dynamics (MD) simulation. Based on the results of this study, 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors.     

Investigation of silent information regulator 1 (Sirt1) agonists from Traditional Chinese Medicine

Silent information regulator 1 (Sirt1), a class III nicotinamide adenine dinucleotide dependent histone deacetylases, is important in cardioprotection, neuroprotection, metabolic disease, calorie restriction, and diseases associated with aging. Traditional Chinese Medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed for screening potent Sirt1 agonists, and molecular dynamics (MD) simulation was implemented to simulate ligand optimum docking poses and protein structure under dynamic conditions. TCM compounds such as (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA exhibited good binding affinity across different computational methods, and their drug-like potential were validated by MD simulation. Docking poses indicate that the carboxylic group of the three candidates generated H-bonds with residues in the protein chain from Ser441 to Lys444 and formed H-bond, π–cation interactions, or hydrophobic contacts with Phe297 and key active residue, His363. During MD, stable π–cation interactions with residues Phe273 or Arg274 were formed by (S)-tryptophan-betaxanthin and RosA. All candidates were anchored to His363 by stable π- or H-bonds. Hence, we propose (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA as potential lead compounds that can be further tested in drug development process for diseases associated with aging

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:28     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

A possible strategy against head and neck cancer: in silico investigation of three-in-one inhibitors

Overexpression of epidermal growth factor receptor (EGFR), Her2, and uroporphyrinogen decarboxylase (UROD) occurs in a variety of malignant tumor tissues. UROD has potential to modulate tumor response of radiotherapy for head and neck cancer, and EGFR and Her2 are common drug targets for the treatment of head and neck cancer. This study attempts to find a possible lead compound backbone from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) for EGFR, Her2, and UROD proteins against head and neck cancer using computational techniques. Possible traditional Chinese medicine (TCM) lead compounds had potential binding affinities with EGFR, Her2, and UROD proteins. The candidates formed stable interactions with residues Arg803, Thr854 in EGFR, residues Thr862, Asp863 in Her2 protein, and residues Arg37, Arg41 in UROD protein, which are key residues in the binding or catalytic domain of EGFR, Her2, and UROD proteins. Thus, the TCM candidates indicated a possible molecule backbone for evolving potential inhibitors for three drug target proteins against head and neck cancer.

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:35     

3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses using CoMFA and CoMSIA methods were conducted on a series of fluoropyrrolidine amides as dipeptidyl peptidase IV (DP-IV) inhibitors. The selected ligands were docked into the binding site of the 3D model of DP-IV using the GOLD software, and the possible interaction models between DP-IV and the inhibitors were obtained. Based on the binding conformations of these fluoropyrrolidine amides and their alignment inside the binding pocket of DP-IV, predictive 3D-QSAR models were established by CoMFA and CoMSIA analyses, which had conventional r ² and cross-validated coefficient values () up to 0.982 and 0.555 for CoMFA and 0.953 and 0.613 for CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were in the testing set. Structure-based investigations and the final 3D-QSAR results provide the guide for designing new potent inhibitors.     

Three-in-one agonists for PPAR-α, PPAR-γ, and PPAR-δ from traditional Chinese medicine

Nowadays, the occurrence of metabolic syndrome, which is characterized by obesity and clinical disorders, has been increasing rapidly over the world. It induces several serious chronic diseases such as cardiovascular disease, dyslipidemia, gall bladder disease, hypertension, osteoarthritis, sleep apnea, stroke, and type 2 diabetes mellitus. Peroxisome proliferator-activated receptors (PPARs), which have three isoforms: PPAR-α, PPAR-γ, and PPAR-δ, are key regulators of adipogenesis, lipid and carbohydrate metabolism, and are potential drug targets for treating metabolic syndrome. The traditional Chinese medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed to virtually screen for potential PPAR agonists, and structure-based pharmacophore models were generated to identify the key interactions for each PPAR protein. In addition, molecular dynamics (MD) simulation was performed to evaluate the stability of the PPAR–ligand complexes in a dynamic state. (S)-Tryptophan-betaxanthin and berberrubine, which have higher Dock Score than controls, form stable interactions during MD, and are further supported by the structure-based pharmacophore models in each PPAR protein. Key features include stable H-bonds with Thr279 and Ala333 of PPAR-α, with Thr252, Thr253 and Lys331 of PPAR-δ, and with Arg316 and Glu371 of PPAR-γ. Hence, we propose the top two TCM candidates as potential lead compounds in developing agonists targeting PPARs protein for treating metabolic syndrome.     

Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

CoMFA and CoMSIA 3D-QSAR analysis on hydroxamic acid derivatives as urease inhibitors

Urease (EC 3.5.1.5) serves as a virulence factor in pathogens that are responsible for the development of many diseases in humans and animals. Urease allows soil microorganisms to use urea as a source of nitrogen and aid in the rapid break down of urea-based fertilizers resulting in phytopathiCIT000y. It has been well established that hydroxamic acids are the potent inhibitors of urease activity. The 3D-QSAR studies on thirty five hydroxamic acid derivatives as known urease inhibitors were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The CoMFA model produced statistically significant results with cross-validated (q²) 0.532 and conventional (r²) correlation coefficients 0.969.The model indicated that the steric field (70.0%) has greater influence on hydroxamic acid inhibitors than the electrostatic field (30.0%). Furthermore, five different fields: steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor assumed to generate the CoMSIA model, which gave q² 0.665 and r² 0.976.This model showed that steric (43.0%), electrostatic (26.4%) and hydrophobic (20.3%) properties played a major role in urease inhibition. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible modification of the hydroxamic acid derivatives for improved activity.     

Molecular dynamics analysis of potent inhibitors of M2 proton channel against H1N1 swine influenza virus

The recent H1N1 (swine) influenza pandemic highlighted the urgent need of having effective anti‐viral strategies. In addition to neuraminidase inhibitors, there is another class of anti-viral drug known as M2 inhibitors that were, in the past, effective in treating seasonal influenza. However, due to the emergence of M2 inhibitor‐resistant influenza viruses, this class of drugs was not recommended for clinical usage in the latest influenza pandemic. In order to identify novel M2 inhibitors, we have performed molecular docking using a traditional Chinese medicine database (http://tcm.cmu.edu.tw/index.php). Docking and subsequent de novo designs gave 10 derivatives that have much better docking results than the control. Of these 10 derivatives, the top three, methyl isoferulate_1, genipin_1 and genipin_2, were selected for molecular dynamics simulation. During the simulation run, the top three derivatives all had stable interactions with M2 residues, Ser31 and Ala30. Methyl isoferulate_1 also has stable interaction to His37. Therefore, we recommend these three derivatives for further biomolecular experiments and clinical studies.     

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

Abstract

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q²) of 0.784 and 0.768, respectively, and conventional coefficients (r²) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.     

3D-QSAR and molecular docking studies of azaindole derivatives as Aurora B kinase inhibitors

The Aurora kinases have been regarded as attractive targets for the development of new anticancer agents. Recently a series of azaindole derivatives with Aurora B inhibitory activities were reported. To explore the relationship between the structures of substituted azaindole derivatives and their inhibition of Aurora B, 3D-QSAR and molecular docking studies were performed on a dataset of 41 compounds. 3D-QSAR, including CoMFA and CoMSIA, were applied to identify the key structures impacting their inhibitory potencies. The CoMSIA model showed better results than CoMFA, with r ² _cv value of 0.575 and r ² value of 0.987. 3D contour maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity. Based on the structure-activity relationship revealed by the present study, we have designed a set of novel Aurora B inhibitors that showed excellent potencies in the developed models. Thus, our results allowed us to design new derivatives with desired activities.     

Uroporphyrinogen Decarboxylase as a Potential Target for Specific Components of Traditional Chinese Medicine: A Virtual Screening and Molecular Dynamics Study

Uroporphyrinogen decarboxylase (UROD) has been suggested as a protectant against radiation for head and neck cancer (HNC). In this study, we employed traditional Chinese medicine (TCM) compounds from TCM nawiaT@esabataD (http://tcm.cmu.edu.tw/) to screen for drug-like candidates with potential UROD inhibition characteristics using virtual screening techniques. Isopraeroside IV, scopolin, and nodakenin exhibited the highest Dock Scores, and were predicted to have good Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Two common moieties, 2H-chromen-2-one and glucoside, were observed among the top TCM candidates. Cross comparison of the docking poses indicated that candidates formed stable interactions with key binding and catalytic residues of UROD through these two moieties. The 2H-chromen-2-one moiety enabled pi-cation interactions with Arg37 and H-bonds with Tyr164. The glucoside moiety was involved in forming H-bonds with Arg37 and Asp86. From our computational results, we propose isopraeroside IV, scopolin, and nodakenin as ligands that might exhibit drug-like inhibitory effects on UROD. The glucoside and 2H-chromen-2-one moieties may potentially be used for designing inhibitors of UROD.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

3D-QSAR studies of checkpoint kinase 1 inhibitors based on molecular docking and CoMFA

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of substituted 1,4-dihydroindeno[1,2-c]pyrazoles inhibitors, using molecular docking and comparative molecular field analysis (CoMFA). The docking results from GOLD 3.0.1 provide a reliable conformational alignment scheme for the 3D-QSAR model. Based on the docking conformations and alignments, highly predictive CoMFA model was built with cross-validated q ² value of 0.534 and non-cross-validated partial least-squares analysis with the optimum components of six showed a conventional r ² value of 0.911. The predictive ability of this model was validated by the testing set with a conventional r ² value of 0.812. Based on the docking and CoMFA, we have identified some key features of the 1,4-dihydroindeno[1,2-c]pyrazoles derivatives that are responsible for checkpoint kinase 1 inhibitory activity. The analyses may be used to design more potent 1,4-dihydroindeno[1,2-c]pyrazoles derivatives and predict their activity prior to synthesis.     

A combined molecular modeling study on a series of pyrazole/isoxazole based human Hsp90α inhibitors

Inhibition of the protein chaperone Hsp90α is a promising approach for cancer therapy. In this work, a molecular modeling study combining pharmacophore model, molecular docking and three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed to investigate a series of pyrazole/isoxazole scaffold inhibitors of human Hsp90α. The pharmacophore model can provide the essential features required for the biological activities of the inhibitors. The molecular docking study can give insight into the binding mode between Hsp90α and its inhibitors. 3D-QSAR based on CoMFA and CoMSIA models were performed from three different strategies for conformational selection and alignment. The receptor-based models gave the most statistically significant results with cross-validated q ² values of 0.782 and 0.829 and r ² values of 0.909 and 0.968, for CoMFA and CoMSIA respectively. Furthermore, the 3D contour maps superimposed within the binding site of Hsp90α could help to understand the pivotal interaction and the structural requirements for potent Hsp90α inhibitors. The results show 4-position of pyrazole/isoxazole ring requires bulky and hydrophobic groups, and bulky and electron repulsion substituent of 5-amides is favorable for enhancing activity. This study will be helpful for the rational design of new potent Hsp90α inhibitors.     

3D-QSAR analysis of a new type of acetylcholinesterase inhibitors

Acetylcholinesterase (AChE) inhibitors are an important class of medicinal agents used for the treatment of Alzheimer’s disease. A screening model of AChE inhibitor was used to evaluate the inhibition of a series of phenyl pentenone derivatives. The assay result showed that some compounds displayed higher inhibitory effects. In order to study the relationship between the bioactivities and the structures, 26 compounds with phenyl pentenone scaffold were analyzed. A 3D-QSAR model was constructed using the method of comparative molecular field analysis (CoMFA). The results of cross-validated R²cv=0.629, non-cross-validated R²=0.972, SE=0.331, and F=72.41 indicate that the 3D-model possesses an ability to predict the activities of new inhibitors, and the CoMFA model would be useful for the future design of new AChE inhibitors.