Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits

This paper describes the generation of ligand-based as well as structure-based models and virtual screening of less toxic P-selectin receptor inhibitors. Ligand-based model, 3D-pharmacophore was generated using 27 quinoline salicylic acid compounds and is used to retrieve the actives of P-selectin. This model contains three hydrogen bond acceptors (HBA), two ring aromatics (RA) and one hydrophobic feature (HY). To remove the toxic hits from the screened molecules, a counter pharmacophore model was generated using inhibitors of dihydrooratate dehydrogenase (DHOD), an important enzyme involved in nucleic acid synthesis, whose inhibition leads to toxic effects. Structure-based models were generated by docking and scoring of inhibitors against P-selectin receptor, to remove the false positives committed by pharmacophore screening. The combination of these ligand-based and structure-based virtual screening models were used to screen a commercial database containing 538,000 compounds.     

In silico study of subtilisin-like protease 1 (SUB1) from different Plasmodium species in complex with peptidyl-difluorostatones and characterization of potent pan-SUB1 inhibitors

Plasmodium falciparum subtilisin-like protease 1 (SUB1) is a novel target for the development of innovative antimalarials. We recently described the first potent difluorostatone-based inhibitors of the enzyme ((4S)-(N-((N-acetyl-l-lysyl)-l-isoleucyl-l-threonyl-l-alanyl)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (1) and (4S)-(N-((N-acetyl-l-isoleucyl)-l-threonyl-l-alanylamino)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (2)). As a continuation of our efforts towards the definition of the molecular determinants of enzyme-inhibitor interaction, we herein propose the first comprehensive computational investigation of the SUB1 catalytic core from six different Plasmodium species, using homology modeling and molecular docking approaches. Investigation of the differences in the binding sites as well as the interactions of our inhibitors 1,2 with all SUB1 orthologues, allowed us to highlight the structurally relevant regions of the enzyme that could be targeted for developing pan-SUB1 inhibitors. According to our in silico predictions, compounds 1,2 have been demonstrated to be potent inhibitors of SUB1 from all three major clinically relevant Plasmodium species (P. falciparum, P. vivax, and P. knowlesi). We next derived multiple structure-based pharmacophore models that were combined in an inclusive pan-SUB1 pharmacophore (SUB1-PHA). This latter was validated by applying in silico methods, showing that it may be useful for the future development of potent antimalarial agents.     

Discovery of high affinity ligands for β2-adrenergic receptor through pharmacophore-based high-throughput virtual screening and docking

Novel high affinity compounds for human β₂-adrenergic receptor (β₂-AR) were searched among the clean drug-like subset of ZINC database consisting of 9,928,465 molecules that satisfy the Lipinski's rule of five. The screening protocol consisted of a high-throughput pharmacophore screening followed by an extensive amount of docking and rescoring. The pharmacophore model was composed of key features shared by all five inactive states of β₂-AR in complex with inverse agonists and antagonists. To test the discriminatory power of the pharmacophore model, a small-scale screening was initially performed on a database consisting of 117 compounds of which 53 antagonists were taken as active inhibitors and 64 agonists as inactive inhibitors. Accordingly, 7.3% of the ZINC database subset (729,413 compounds) satisfied the pharmacophore requirements, along with 44 antagonists and 17 agonists. Afterwards, all these hit compounds were docked to the inactive apo form of the receptor using various docking and scoring protocols. Following each docking experiment, the best pose was further evaluated based on the existence of key residues for antagonist binding in its vicinity. After final evaluations based on the human intestinal absorption (HIA) and the blood brain barrier (BBB) penetration properties, 62 hit compounds have been clustered based on their structural similarity and as a result four scaffolds were revealed. Two of these scaffolds were also observed in three high affinity compounds with experimentally known K_i values. Moreover, novel chemical compounds with distinct structures have been determined as potential β₂-AR drug candidates.     

A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer’s disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells.     

Identification of novel inhibitors against Mycobacterium tuberculosis l-alanine dehydrogenase (MTB-AlaDH) through structure-based virtual screening

Mycobacterium tuberculosis (MTB) the etiological agent of tuberculosis (TB) survives in the human host for decades evading the immune system in a latent or persistent state. The Rv2780 (ald) gene that codes for l-alanine dehydrogenase (l-AlaDH) enzyme catalyzes reversible oxidative deamination of l-alanine to pyruvate and is overexpressed under hypoxic and nutrient starvation conditions in MTB. At present, as there is no suitable drug available to treat dormant tuberculosis; it is essential to identify drug candidates that could potentially treat dormant TB. Availability of crystal structure of MTB l-AlaDH bound with co-factor NAD⁺ facilitated us to employ structure-based virtual screening approach to obtain new hits from a commercial library of Asinex database using energy-optimized pharmacophore modeling. The resulting pharmacophore consisted of three hydrogen bond donor sites (D) and two hydrogen bond acceptor sites (A). The database compounds with a fitness score more than 1.0 were further subjected to Glide high-throughput virtual screening and docking. Thus, we report the identification of best five hits based on structure-based design and their in vitro enzymatic inhibition studies revealed IC₅₀ values in the range of 35–80 μM.     

Integration of ligand and structure based approaches for identification of novel MbtI inhibitors in Mycobacterium tuberculosis and molecular dynamics simulation studies

Mycobacterium tuberculosis is an obligate pathogen of mammals and is responsible for more than two million deaths annually. The ability to acquire iron from the extracellular environment is a key determinant of pathogenicity in mycobacteria. M. tuberculosis acquires iron exclusively through the siderophores. Several lines of evidence suggest that siderophores have a critical role in bacterial growth and virulence. Hence, in the present study, we have used a combined ligand and structure-based drug design approach for identification of novel inhibitors against salicylate synthase MbtI, a unique and essential enzyme for the biosynthesis of siderophores in M. tuberculosis. We have generated the ligand based and structure based pharmacophores and validated exhaustively. From the validation results it was found that GH (Goodness of Hit) scores for the selected ligand based and structure based pharmacophore models were 0.89 and 0.97, respectively, which indicate that the quality of the pharmacophore models are acceptable as GH value is >0.7. The validated pharmacophores were used for screening the ZINC database. A total of 73 hits, obtained through various insilico screening techniques, were further enriched to 17 hits using docking studies. Molecular dynamics simulations were carried out to compare the binding mode and stability of complexes of MbtI bound with substrate, known inhibitors, and three top ranked hits. The results obtained in this study gave assurance about the identified hits as prospective inhibitors of MbtI.     

Structure based virtual screening-driven identification of monastrol as a potent urease inhibitor

Virtual screening uses computer based methods to discover new ligands on the basis of biological structures. Among all virtual screening methods structure based docking has received considerable attention. In an attempt to identify new ligands as urease inhibitors, structure-based virtual screening (SBVS) of an in-house database of 10,000 organic compounds was carried out. The X-ray crystallographic structure of Bacillus pasteurii (BP) in complex with acetohydroxamic acid (PDB Code 4UBP) was used as a protein structure. As a starting point, ~10,000 compounds of our in-house database were analyzed to check redundancy and the compounds found repeated were removed from the database. Finally 6993 compounds were docked into the active site of BP urease using GOLD and MOE-Dock software. A remarkable feature of this study was the identification of monastrol, a well-known KSP inhibitor already in clinical trials, as a novel urease inhibitor. The hits identified were further evaluated by molecular docking and on examination of the affinity predictions, twenty-seven analogs of monastrol were synthesized by a multicomponent Biginelli reaction followed by their in vitro screening as urease inhibitors. Finally twelve compounds were identified as new urease inhibitors. The excellent in vitro activity suggested that these compounds may serve as viable lead compounds for the treatment of urease related problems.     

Computational determination of binding structures and free energies of glucose 6-phosphate dehydrogenase with novel steroid inhibitors

Glucose 6-phosphate dehydrogenase (G6PD), the first and the rate-limiting enzyme in the pentose phosphate pathway (PPP), catalyzes the oxidation of G6P to 6-phosphogluconolactone and the reduction of NADP⁺ to NADPH. Its key role in cancer promotes the development of a potent and selective inhibitor that might increase cancer cell death when combined with radiotherapy. In the present study, we investigated the detailed binding modes and binding free energies for G6PD interacting with a promising series of recently developed inhibitors, i.e., the steroid derivatives, by performing molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. The docking indicates that the inhibitors occupy the binding sites of both G6P and NADP⁺. The calculated binding free energies on the basis of the MD-simulated enzyme–inhibitor complexes are in good agreement with the experimental activity data for all of the examined inhibitors. The valuable insights into the detailed enzyme–inhibitor binding including the important intermolecular interactions, e.g., the hydrogen bond interaction and the hydrophobic interaction, have been provided. The computational results provide new insights into future rational design of more potent inhibitors of G6PD as a treatment for cancer.     

Structure-based design of hERG-neutral antihypertensive oxazalone and imidazolone derivatives

Angiotensin II receptor type 1 (AT1) antagonists are the most recent drug class against hypertension. Recently first crystal structure of AT1 receptor is deposited to the protein data bank (PDB ID: 4YAY). In this work, several molecular screening methods such as molecular docking and de novo design studies were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. A database consisting of 3500-fragments were used to enumerate de novo designed imidazolone and oxazolone derivatives and hereby more than 50000 novel small molecules were generated. These derivatives were then used in high throughput virtual screening simulations (Glide/HTVS) to find potent hit molecules. In addition, virtual screening of around 18 million small drug-like compounds from ZINC database were screened at the binding pocket of the AT1 receptor via Glide/HTVS method. Filtered structures were then used in more sophisticated molecular docking simulations protocols (i.e., Glide/SP; Glide/XP; Glide/IFD; Glide/QPLD, and GOLD). However, the K⁺ ion channel/drug interactions should also be considered in studies implemented in molecular level against their cardiovascular risks. Thus, selected compounds with high docking scores via all diverse docking algorithms are also screened at the pore domain regions of human ether-a-go-go-related gene (hERG1) K⁺ channel to remove the high affinity hERG1 blocking compounds. High docking scored compounds at the AT1 with low hERG1 affinity is considered for long molecular dynamics (MD) simulations. Post-processing analysis of MD simulations assisted for better understanding of molecular mechanism of studied compounds at the binding cavity of AT1 receptor. Results of this study can be useful for designing of novel and safe AT1 inhibitors.     

Novel flavonol analogues as potential inhibitors of JMJD3 histone demethylase—A study based on molecular modelling

Epigenetic modulation of gene expression has drawn enormous attention among researchers globally in the present scenario. Since their discovery, Jmj-C histone demethylases were identified as useful markers in understanding the role of epigenetics in inflammatory conditions and in cancer as well. This has created arousal of interest in search of suitable candidates. Potential inhibitors from various other scaffolds such as hydroxyquinolines, hydroxamic acids and triazolopyridines have already been identified and reported. In this direction, our present study attempts to target one of the important members of the family- namely JMJD3 (also known as KDM6B), that plays a pivotal role in inflammatory and immune reactions. Using molecular modeling approaches, myricetin analogues were identified as promising inhibitors of JMJD3. Extensive literature review showed myricetin as the most promising flavonol inhibitor for this enzyme. It served as a prototype for our study and modification of it’s scaffold led to generation of analogues. The ZINC database was used as a repository for natural compounds and their analogues. Using similarity search options, 65 analogues of myricetin were identified and screened against JMJD3 (PDB ID: 4ASK), using the high throughput virtual screening and ligand docking tools in Maestro Molecular Modeling platform (version 10.5) from Schrödinger, LLC. 8 analogues out of 65 were identified as the most appropriate candidates which gave the best pose in ligand docking. Their binding mode and energy calculations were analysed using induced fit docking (IFD) and prime-MMGBSA tool, respectively. Thus, our findings highlight the most promising analogues of myricetin with comparable binding affinity as well as binding energy than their counterparts that could be taken for further optimisation as inhibitors of JMJD3 in both in vitro and in vivo screening studies.     

Multicomplex-based pharmacophore modeling coupled with molecular dynamics simulations: An efficient strategy for the identification of novel inhibitors of PfDHODH

Enormous efforts have been made in the past to identify novel scaffolds against the potential therapeutic target, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH). Fourteen different organic molecules have been crystallized to understand the structural basis of the inhibition. However, the pharmacophoric studies carried out so far, have not exploited all the structural information simultaneously to identify the novel inhibitors. Therefore, an attempt was made to construct the pharmacophore hypotheses from the available PfDHODH structural proteome. Among the generated hypotheses, a representative hypothesis was employed as a primary filter to list the molecules with complimentary features accountable for inhibition. Moreover, the auxiliary evaluations of the filtered molecules were accomplished via docking and drug-likeness studies. Subsequently, the stability of the protein-ligand complex was evaluated by using molecular dynamics simulations (MDs). The molecular details of binding interactions of the potential hits were compared with the highly active experimental structure (5FI8) to seek the more potent candidates that can be targeted against PfDHODH. Overall, the combination of screening and stability procedures resulted in the identification of three potent candidates. The drug-likeness of these molecules lie within the acceptable range and consequently increased the opportunities for their development to new anti-malarials.     

Combined in silico approaches for the identification of novel inhibitors of human islet amyloid polypeptide (hIAPP) fibrillation

Human islet amyloid polypeptide (hIAPP) is a natively unfolded polypeptide hormone of glucose metabolism, which is co-secreted with insulin by the β-cells of the pancreas. In patients with type 2 diabetes, IAPP forms amyloid fibrils because of diabetes-associated β-cells dysfunction and increasing fibrillation, in turn, lead to failure of secretory function of β-cells. This provides a target for the discovery of small organic molecules against protein aggregation diseases. However, the binding mechanism of these molecules with monomers, oligomers and fibrils to inhibit fibrillation is still an open question. In this work, ligand and structure-based in silico approaches were used to identify novel fibrillation inhibitors and/or fibril binding compounds. The best pharmacophore model was used as a 3D search query for virtual screening of a compound database to identify novel molecules having the potential to be therapeutic agents against protein aggregation diseases. Docking and molecular dynamics simulation studies were used to explore the interaction pattern and mechanism of the identified novel small molecules with predicted hIAPP structure, its aggregation prone conformation and fibril forming segments. We show that catechins with galloyl group and molecules having two to three planar apolar rings bind to hIAPP structures and fibril forming segments with greater affinity. The differences in binding affinities of different compounds against several fibril forming segments of the peptide suggest that a mixture of active compounds may be required for treatment of aggregation diseases.     

Identification of potent urease inhibitors via ligand- and structure-based virtual screening and in vitro assays

A pharmacophore model was developed based on three structurally diverse urease inhibitors by using the GASP program. This model comprises the positions and tolerance for two acceptor atoms (AA1 and AA2), one donor atom (DA1), and one hydrophobic center (HYP1). This derived phamacophore model was employed to screen an in-house database of organic compounds. Hits obtained were evaluated by molecular docking using GOLD software. On the basis of ligand- and structural-based predictions, an in vitro testing of short-listed compounds was conducted and a novel class of urease inhibitors (2-aminothiophines) was identified. The potent in vitro activity and selectivity of these compounds, along with their non-toxic nature against the plant cells indicated that they can serve as leads for solving urease-associated health and agriculture problems.     

New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking

Discovery of new inhibitors of the protein associated with a given disease is the initial and most important stage of the whole process of the rational development of new pharmaceutical substances. New inhibitors block the active site of the target protein and the disease is cured. Computer-aided molecular modeling can considerably increase effectiveness of new inhibitors development. Reliable predictions of the target protein inhibition by a small molecule, ligand, is defined by the accuracy of docking programs. Such programs position a ligand in the target protein and estimate the protein-ligand binding energy. Positioning accuracy of modern docking programs is satisfactory. However, the accuracy of binding energy calculations is too low to predict good inhibitors. For effective application of docking programs to new inhibitors development the accuracy of binding energy calculations should be higher than 1 kcal/mol. Reasons of limited accuracy of modern docking programs are discussed. One of the most important aspects limiting this accuracy is imperfection of protein-ligand energy calculations. Results of supercomputer validation of several force fields and quantum-chemical methods for docking are presented. The validation was performed by quasi-docking as follows. First, the low energy minima spectra of 16 protein-ligand complexes were found by exhaustive minima search in the MMFF94 force field. Second, energies of the lowest 8192 minima are recalculated with CHARMM force field and PM6-D3H4X and PM7 quantum-chemical methods for each complex. The analysis of minima energies reveals the docking positioning accuracies of the PM7 and PM6-D3H4X quantum-chemical methods and the CHARMM force field are close to one another and they are better than the positioning accuracy of the MMFF94 force field.     

Pharmacophore modeling,virtual screening,docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors

Protein kinase B (PKB) is a key mediator of proliferation and survival pathways that are critical for cancer growth. Therefore, inhibitors of PKB are useful agents for the treatment of cancer. Herein, we describe pharmacophore-based virtual screening combined with docking study as a rational strategy for identification of novel hits or leads. Pharmacophore models of PKB β inhibitors were established using the DISCOtech and refined with GASP from compounds with IC₅₀ values ranging from 2.2 to 246 nM. The best pharmacophore model consists of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) site and two hydrophobic (HY) features. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods indicated that the model-3 was statistically valuable and reliable in identifying PKB β inhibitors. Pharmacophore model as a 3D search query was searched against NCI database. Several compounds with different structures (scaffolds) were retrieved as hits. Molecules with a Q_fit value of more than 95 and three other known inhibitors were docked in the active site of PKB to further explore the binding mode of these compounds. Finally in silico pharmacokinetic and toxicities were predicted for active hit molecules. The hits reported here showed good potential to be PKB β inhibitors.     

Homology models of the mutated EGFR and their response towards quinazoline analogues

One of the most intensely studied tyrosine kinases is the epidermal growth factor receptor (EGFR). The tyrosine kinase receptors are known to be over expressed in some solid tumors and non-small cell lung cancers, causing differential susceptibility to the quinazoline inhibitors. In this study we have taken SYK tyrosine kinase coordinates from PDB database to model two new EGFR receptors with these mutations G695S and L834R and conducted all the docking studies of the inhibitors, also evaluated these two models for quality of structure using PROCHECK. Seven quinazoline analogues (gefitinib, erlotinib, CI-1033, and EKB-569 and other analogues) were selected for comparisons among the two new models. This study determined the receptor/inhibitor interactions, at that active domain binding sites consisting of 15 amino acids. We were able to calculate the energy data for each of the seven inhibitors. This data has been important in interpreting the affinity between the inhibitors evaluated against the three models of EGFR (wild-type and two mutated types). "Affinity"-based studies have indicated the order of response based on docking energy levels (Van der Waals and electrostatic interactions). The active ATP binding sites consisting of 15 amino acid residues were identified and the total energy (E(total)) which showed the affinity between the inhibitor molecules and the receptor (Van der Waals and electrostatic interactions). The selection of the quinazoline analogues was purely on their emergence as possible candidates in the drug discovery areas. This study describes the successful application of these models that we constructed for molecular docking studies to rationally design compounds predicted to bind favorably to the modeled EGFR catalytic sites.     

Binding mode analyses and pharmacophore model development for sulfonamide chalcone derivatives,a new class of α-glucosidase inhibitors

Sulfonamide chalcone derivatives are a new class of non-saccharide compounds that effectively inhibit glucosidases which are the major targets in the treatment of Type 2 diabetes and HIV infection. Our aim is to explore their binding mode of interaction at the active site by comparing with the sugar derivatives and to develop a pharmacophore model which would represent the critical features responsible for α-glucosidase inhibitory activity. The homology modeled structure of Saccharomyces cerevisiae α-glucosidase was built and used for molecular docking of non-sugar/sugar derivatives. The validated docking results projected the crucial role of NH group in the binding of sugar/non-sugar derivatives to the active site. Ligplot analyses revealed that Tyr71, and Phe177 form hydrophobic interactions with sugar/non-sugar derivatives by holding the terminal glycosidic ring mimics. Molecular dynamic (MD) simulation studies were performed for protein alone and with chalcone derivative to prove its binding mechanism as shown by docking/Ligplot results. It would also help to substantiate the homology modeled structure stability. With the knowledge of the crucial interactions between ligand and protein from docking and MD simulation studies, features for pharmacophore model development were chosen. The CATALYST/HipHop was used to generate a five featured pharmacophore model with a training set of five non-sugar derivatives. As validation, all the crucial features of the model were perfectly mapped onto the 3D structures of the sugar derivatives as well as the newly tested non-sugar derivatives. Thus, it can be useful in virtual screening for finding new non-sugar derivatives as α-glucosidase inhibitors.     

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC₅₀ value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC₅₀ values from 5.5 to 6.5.     

Discovery of potent inhibitor for matrix metalloproteinase-9 by pharmacophore based modeling and dynamics simulation studies

Matrix metalloproteinase-9 (MMP-9) is an attractive target for anticancer therapy. In the present study ligand based pharmacophore modeling was performed to elucidate the structural elements for a diverse class of MMP-9 inhibitors. The pharmacophore model was validated through Güner-Henry (GH) scoring method. The final pharmacophore model consisted of three hydrogen bond acceptors (HBA), and two ring aromatic regions (RA). This model was utilized to screen the natural compound database to seek novel compounds as MMP-9 inhibitors. The identified hits were validated using molecular docking and molecular dynamics simulation studies. Finally, one compound named Hinokiflavone from Juniperus communis had high binding free energy of −26.54 kJ/mol compared with the known inhibitors of MMP-9. Cytotoxicity for hinokiflavone was evaluated by MTT assay. Inhibition of MMP-9 in the presence of hinokiflavone was detected by gelatin zymography and gelatinolytic inhibition assay. Results revealed that the natural compounds derived based on the developed pharmacophore model would be useful for further design and development of MMP-9 inhibitors.     

Identification of potential bivalent inhibitors from natural compounds for acetylcholinesterase through in silico screening using multiple pharmacophores

The symptomatic cure observed in the treatment of Alzheimer's disease (AD) by FDA approved drugs could possibly be due to their specificity against the active site of acetylcholinesterase (AChE) and not by targeting its pathogenicity. The AD pathogenicity involved in AChE protein is mainly due to amyloid beta peptide aggregation, which is triggered specifically by peripheral anionic site (PAS) of AChE. In the present study, a workflow has been developed for the identification and prioritization of potential compounds that could interact not only with the catalytic site but also with the PAS of AChE. To elucidate the essential structural elements of such inhibitors, pharmacophore models were constructed using PHASE, based on a set of fifteen best known AChE inhibitors. All these models on validation were further restricted to the best seven. These were transferred to PHASE database screening platform for screening 89,425 molecules deposited at the “ZINC natural product database”. Novel lead molecules retrieved were subsequently subjected to molecular docking and ADME profiling. A set of 12 compounds were identified with high pharmacophore fit values and good predicted biological activity scores. These compounds not only showed higher affinity for catalytic residues, but also for Trp86 and Trp286, which are important, at PAS of AChE. The knowledge gained from this study, could lead to the discovery of potential AChE inhibitors that are highly specific for AD treatment as they are bivalent lead molecules endowed with dual binding ability for both catalytic site and PAS of AChE.