Protein-ligand Interaction Fingerprints Research Articles

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking.

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Multi-stage structure-based virtual screening approach towards identification of potential SARS-CoV-2 NSP13 helicase inhibitors

On account of its crucial role in the virus life cycle, SARS-COV-2 NSP13 helicase enzyme was exploited as a promising target to identify a novel potential inhibitor using multi-stage structure-based drug discovery approaches. Firstly, a 3D pharmacophore was generated based on the collected data from a protein-ligand interaction fingerprint (PLIF) study using key interactions between co-crystallised fragments and the NSP13 helicase active site. The ZINC database was screened through the generated 3D-pharmacophore retrieving 13 potential hits. All the retrieved hits exceeded the benchmark score of the co-crystallised fragments at the molecular docking step and the best five-hit compounds were selected for further analysis. Finally, a combination between molecular dynamics simulations and MM-PBSA based binding free energy calculations was conducted on the best hit (compound FWM-1) bound to NSP13 helicase enzyme, which identified FWM-1 as a potential potent NSP13 helicase inhibitor with binding free energy equals −328.6 ± 9.2 kcal/mol.

In Silico Prediction Methods for Site-Saturation Mutagenesis.

Directed enzyme evolution has proven to be a powerful means to endow biocatalysts with novel catalytic repertoires. Apart from completely random gene mutagenesis, site-directed or site-saturation mutagenesis requires a semi-rational selection of the amino acid positions or the substituted residues, which can dramatically reduce the screening efforts in protein engineering. To this end, in silico prediction methods play a pivotal role in targeting site-saturation mutagenesis. In this chapter, we provide two distinct computational methods, (a) conformational dynamics-guided design and (b) protein-ligand interaction fingerprinting analysis, to identify specific positions for site-saturation mutagenesis toward manipulating substrate specificity/stereoselectivity of an alcohol dehydrogenase, and improving activity of a carboxylic acid reductase, respectively.

Encoding mu-opioid receptor biased agonism with interaction fingerprints.

Opioids are potent painkillers,however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the μ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUCMAX = 0.86 ± 0.01) and negative predictive values (NPVMAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Structure-Activity Relationship Analysis of Cocrystallized Gliptin-like Pyrrolidine, Trifluorophenyl, and Pyrimidine-2,4-Dione Dipeptidyl Peptidase-4 Inhibitors.

Approved and potent reported dipeptidyl peptidase-4 (DPP-4) inhibitors with gliptin-like structures are classified here according to their structures and mechanisms of the inhibition in three groups: (i) those with pyrrolidine or analogs as P1 fragment with α-aminoacyl linker, (ii) structures with trifluorophenyl moiety or analogs as P1 fragment with β-aminobutanoyl linker, and (iii) DPP-4 inhibitors with pyrimidine-2,4-dione or analogs as P1' fragment. The structure-activity relationship analysis was performed for those whose cocrystallized structures with the enzyme were published. While inhibitors with pyrrolidine and trifluorophenyl moiety or analogs as P1 fragment bind in a similar way in S1, S2 and S2 extensive domains of the enzyme, the binding mode of pyrimidine-2,4-dione derivatives/analogs differs with additional interactions in S1' and S2' pockets. Three general schemes of fragmented gliptins and gliptin-like structures with the enzyme and protein-ligand interaction fingerprints were made, which might be useful in the creation of DPP-4 inhibitor's design strategies.

Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation.

The triple reuptake inhibitors (TRIs) class is a class of effective inhibitors of human monoamine transporters (hMATs), which includes dopamine, norepinephrine, and serotonin transporters (hDATs, hNETs, and hSERTs). Due to the high degree of structural homology of the binding sites of those transporters, it is a great challenge to design potent TRIs with fine-tuned binding profiles. The molecular determinants responsible for the binding selectivity of TRIs to hDATs, hNETs, and hSERTs remain elusive. In this study, the solved X-ray crystallographic structure of hSERT in complex with escitalopram was used as a basis for modeling nine complexes of three representative TRIs (SEP225289, NS2359, and EB1020) bound to their corresponding targets. Molecular dynamics (MD) and effective post-trajectory analysis were performed to estimate the drug binding free energies and characterize the selective profiles of each TRI to hMATs. The common binding mode of studied TRIs to hMATs was revealed by hierarchical clustering analysis of the per-residue energy. Furthermore, the combined protein-ligand interaction fingerprint and residue energy contribution analysis indicated that several conserved and nonconserved "Warm Spots" such as S149, V328, and M427 in hDAT, F317, F323, and V325 in hNET and F335, F341, and V343 in hSERT were responsible for the TRI-binding selectivity. These findings provided important information for rational design of a single drug with better polypharmacological profiles through modulating multiple targets.

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors.

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

Discovery of Novel CCR5 Ligands as Anticolorectal Cancer Agents by Sequential Virtual Screening.

C-C chemokine receptor type 5 (CCR5) is a member of the G protein-coupled receptor. CCR5 and its interaction with chemokine ligands have been crucial for understanding and tackling human immunodeficiency virus (HIV)-1 entry into target cells. In recent years, the change in CCR5 expression has been related to the progression of different cancer types. Patients treated with the CCR5 ligand, maraviroc (MVC), showed a deceleration in tumor development especially for metastatic colorectal cancer. Based on the crystal structure of CCR5, we herein describe a multistage virtual screening protocol including pharmacophore screening, molecular docking, and protein–ligand interaction fingerprint (PLIF) postdocking filtration for discovery of novel CCR5 ligands. The applied virtual screening protocol led to the identification of four hits with binding modes showing access to the major and minor pockets of the MVC binding site. Compounds 2–4 showed a decrease in cellular proliferation upon testing on the metastatic colorectal cancer cell line, SW620, displaying 12, 16, and 4 times higher potency compared to MVC, respectively. Compound 3 induced apoptosis by arresting cells in the G0/G1 phase of the cell cycle similar to MVC. Further in vitro assays showed compound 3 drastically decreasing the CCR5 expression and cellular migration 48 h post treatment, indicating its ability to inhibit metastatic activity in SW620 cells. The discovered hits represent potential leads for the development of novel classes of anticolorectal cancer agents targeting CCR5.

Structure and ligand-based drug discovery of IL-4 inhibitors via interaction-energy-based learning approaches

Interleukin-4 (IL-4), an anti-inflammatory cytokine plays significant in the development of various diseases especially asthmatic allergies. Previous structural and functional studies of IL-4 with its receptor bring forth different types of inhibitors to block their interaction but each of them failed in clinical trials. Since, no synthetic molecules have been identified against IL-4, so far. Therefore, 21 in-house tested IL-4 inhibitors were blindly docked over the entire surface of IL-4 to predict a suitable and druggable binding site as the crystal structure of IL-4 protein in complex with ligand has not been reported yet. After binding site prediction, both ligand-based and structure-based pharmacophore were generated to screen three ZINC libraries (24.5 M) i.e. purchasable, natural product and natural derivative. A total 5,800 top-scored compounds were further subjected towards score-based screening to find the potential leads. Following protein-ligand interaction fingerprints (PLIF) and molecular visualization of selected hits, six top-scored compounds (five from purchasable and one from natural product library) were further moved towards their stability dynamics, followed by their absolute binding free energy and residue-based energy decomposition calculation by MM-GBSA method. These efforts help us to reveal the key factors responsible for ligand binding that might help to improve the binding and stability of these newly discovered hits by structural modifications. Communicated by Freddie R. Salsbury

Repurposing Known Drugs as Covalent and Non-covalent Inhibitors of the SARS-CoV-2 Papain-Like Protease.

In the absence of an approved vaccine, developing effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antivirals is essential to tackle the current pandemic health crisis due to the coronavirus disease 2019 (COVID-19) spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, in silico repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. We present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multitarget activities (i.e., a desirable polypharmacology profile) for the COVID-19 treatment. A dataset including 688 phase III and 1,702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein–ligand interaction fingerprint similarities, conventional docking scores, and MM-GBSA–binding free energies and allowed the identification of some interesting candidates for further in vitro testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.

A multi-stage virtual screening of FDA-approved drugs reveals potential inhibitors of SARS-CoV-2 main protease

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. Repurposing of approved pharmaceutical drugs for COVID-19 treatment represents an attractive approach to quickly identify promising drug candidates. SARS-CoV-2 main protease (Mpro) is responsible for the maturation of viral functional proteins making it a key antiviral target. Based on the recently revealed crystal structures of SARS-CoV-2 Mpro, we herein describe a multi-stage virtual screening protocol including pharmacophore screening, molecular docking and protein-ligand interaction fingerprints (PLIF) post-docking filtration for efficient enrichment of potent SARS-CoV-2 Mpro inhibitors. Potential hits, along with a cocrystallized control were further studied via molecular dynamics. A 150-ns production trajectory was followed by RMSD, free energy calculation, and H-bond analysis for each compound. The applied virtual screening protocol led to identification of five FDA-approved drugs with promising binding modes to key subsites of the substrate-binding pocket of SARS-CoV-2 Mpro. The identified compounds belong to different pharmaceutical classes, including several protease inhibitors, antineoplastic agents and a natural flavonoid. The drug candidates discovered in this study present a potential extension of the recently reported SARS-CoV-2 Mpro inhibitors that have been identified using other virtual screening protocols and may be repurposed for COVID-19 treatment.

A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories.

The dissociation of ligands from proteins and other biomacromolecules occurs over a wide range of timescales. For most pharmaceutically relevant inhibitors, these timescales are far beyond those that are accessible by conventional molecular dynamics (MD) simulation. Consequently, to explore ligand egress mechanisms and compute dissociation rates, it is necessary to enhance the sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site, which enables the exploration of ligand egress routes without prior knowledge of the reaction coordinates. Furthermore, the τRAMD procedure can be used to compute the relative residence times of ligands. When combined with a machine-learning analysis of protein-ligand interaction fingerprints (IFPs), molecular features that affect ligand unbinding kinetics can be identified. Here, we describe the implementation of RAMD in GROMACS 2020, which provides significantly improved computational performance, with scaling to large molecular systems. For the automated analysis of RAMD results, we developed MD-IFP, a set of tools for the generation of IFPs along unbinding trajectories and for their use in the exploration of ligand dynamics. We demonstrate that the analysis of ligand dissociation trajectories by mapping them onto the IFP space enables the characterization of ligand dissociation routes and metastable states. The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of τRAMD in drug design.

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein-Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein-ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design.

Virtual Screening Based on Pharmacophore to Discover Host ER Alpha-Glucosidase II Inhibitor for Dengue Therapy

Dengue is one of the crucial diseases in human-caused by dengue virus (DENV) infection. However, the development of DENV antiviral is often facing a problem because no effective drug to treat infection caused by all DENV serotypes. The inhibition of host protein involved in DENV life cycle can be a potential approach in dengue drug discovery, and also avoiding antiviral resistance. Endoplasmic Reticulum (ER) α-glucosidase II is one of the target host protein in DENV endoplasmic reticulum that plays an important role in the maturation process of DENV envelope glycoprotein. Natural products have been known as an essential source of a lead compound for drug discovery due to their therapeutic potency. In this research, pharmacophore-based virtual screening and molecular docking simulations were performed to find ligand that has potential to inhibit α-glucosidase II activity. About 67,609 natural products from InterBioScreen (IBS) database were used in the simulation as ligands with α-glucosidase II as the protein target. After subjected to Lipinski’s Rule of Five, druglikeness, nasty functions, and toxicity screening using DataWarrior software, 17,462 ligands were obtained. The pharmacophore features for molecular docking simulation was obtained from Protein-Ligand Interaction Fingerprint (PLIF) analysis using eight α-glucosidase II protein with different ligands. Based on virtual screening, rigid, and flexible docking simulations using Molecular Operating Environment (MOE) software, 32 ligands have lower Gibbs free binding energy (ΔGbinding) compared to the standards. Two best ligands, namely STOCK1N-85545 and STOCK1N-86400 which belong alkaloid derivatives, showed the exceptional ligand interaction and had the lowest ΔGbinding of-11.204 and-10.276 kcal/mol, respectively. The ligands were identified to have a binding interaction with amino acid Asp564 and Asp640 in α-glucosidase II catalytic site. STOCK1N-85545 and STOCK1N-86400 were also identified to have a good pharmacological properties after subjected to ADME-tox test using Toxtree, SwissADME, admetSAR, and pkCSM software.

In silico Identification of Peptide as Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Lung Cancer Treatment.

Epidermal growth factor receptor (EGFR) is the biomarker for lung cancer in which the protein has the most active mutated genes in lung cancer patients. Peptides have pharmacological potential as drugs because of their bioactivity and accessibility. The research objective was to obtain peptide compounds drug candidates with good interaction and pharmacological properties that can act as an inhibitor for EGFR for lung cancer treatment by using in silico method. EGFR protein structure was obtained from Protein Data Bank and the peptide compounds were retrieved from PubChem. Optimization and energy minimization process were done to prepare the peptides for the simulation. Protein-Ligand Interaction Fingerprint (PLIF) was used to determine the pharmacophore features in the EGFR binding site. Both proteins and ligands underwent a virtual screening through rigid and flexible molecular docking simulation and the best ligands were subjected to a computational ADME-Tox properties prediction. After screening through molecular docking simulation, nine best compounds were identified to have a good interaction with EGFR protein according to its binding energy and RMSD value. The compounds were identified to form hydrogen bond interactions with the macromolecule. Two peptide compounds (PubChem ID: 20832941 and 9805315) have been predicted as the best ligands with desired pharmacological properties for the inhibition of EGFR tyrosine kinase.

Pharmacophore Modeling, Synthesis, Scaffold Hopping and Biological β- Hematin Inhibition Interaction Studies for Anti-malaria Compounds.

A series of chloroquine analogues were designed using a repositioning approach to develop new anticancer compounds. Protein-ligand interaction fingerprints and ADMET descriptors were used to assess docking performance in virtual screenings to design chloroquine hybrid β-hematin inhibitors. A PLS algorithm was applied to correlate the molecular descriptors to IC50 values. The modeling presented excellent predictive power with correlation coefficients for calibration and cross-validation of r2 = 0.93 and q2 = 0.72. Using the model, a series of 4-aminoquinlin hybrids were synthesized and evaluated for their biological activity as an external test series. These compounds were evaluated for cytotoxic cell lines and β-hematin inhibition. The target compounds exhibited high β-hematin inhibition activity and were 3-9 times more active than the positive control. Furthermore, all the compounds exhibited moderate to high cytotoxic activity. The most potent compound in the dataset was docked with hemoglobin and its pharmacophore features were generated. These features were used as input to the Pharmit server for screening of six databases. The compound with the best score from ChEMBL was 2016904, previously reported as a VEGFR-2 inhibitor. The 11 compounds selected presented the best Gold scores with drug-like properties and can be used for drug development.

In silico approaches to drug repositioning for COVID-19 at AMED-BINDS

Abstract In silico prediction based on the protein structures of SARS-CoV2 is effective to find the putative drug candidates from the approved drugs, as drug repositioning The main protease, 3CL protease, of SARS-Cov2 is essential for proteolytic maturation of the virus, and inhibiting its function could prevent the COVID-19 spreading Here, recent activities in the in-silico unit of AMED-BINDS are introduced Hirokawa et al adopted an in silico docking-based screening approach, which combines molecular docking with a protein-ligand interaction fingerprint (PLIF) scoring method, utilizing the crystal structure of SARS-Cov2 3CL protease (PDB: 6LU7) and a database of known drugs (KEGG-Drug) Selected drugs have the binding modes similar to PLIF of the known active N3 inhibitors with favorable docking scores They identified one hundred and several dozen potentially candidate drugs for 3CL protease inhibitors, which are already approved as antiviral, HIV protease inhibitors, antibacterial or antineoplastic agents Sekijima et al analyzed the interactions between 3CL protease and the drug candidate compounds using molecular dynamics simulation Through this study, they aim to elucidate the interactions between 3CL protease and the drugs The chemical compound libraries in AMED-BINDS will also be available in the future assay studies

Assessment of Risks of Dioxins for Aryl Hydrocarbon Receptor-Mediated Effects in Polar Bear (Ursus maritimus) by in Vitro and in Silico Approaches

Polar bear (Ursus maritimus) populations accumulate dioxins and related compounds (DRCs) at levels that are of health concern. The toxicities of DRCs are primarily mediated via aryl hydrocarbon receptor (AHR) signaling pathway. To evaluate the sensitivity and responses to DRCs in polar bears, we assessed the activation potencies of polar bear-specific AHR (pbAHR) by DRCs through in vitro and in silico approaches. In vitro assays showed that the pbAHR was as sensitive to DRCs as C3H/lpr mouse AHR, which is well known to be highly sensitive to DRCs. Comparison of pbAHR transactivation potencies indicated that TCDF, 2,3,4,7,8-PeCDF, and BaP exhibited high induction equivalency factors (IEFs). PCB126 was found to be the most active inducer of pbAHR. The in vitro transactivation potencies of ligands of pbAHR showed a significant relationship with in silico ligand docking energies in a pbAHR homology model. The protein ligand interaction fingerprint (PLIF) analysis showed different interaction patterns depending on the ligands. Several amino acids which are highly conserved among mammals, may be involved in species-specific responses via backbone interactions with neighboring amino acid residues which are specific to pbAHR. We document increased susceptibility of polar bears to DRCs, through a mechanistic approach, for the first time.

Identification of dual site inhibitors of tankyrase through virtual screening of protein-ligand interaction fingerprint (PLIF)–derived pharmacophore models, molecular dynamics, and ADMET studies

Tankyrases are the group of poly (ADP-ribosyl) polymerases (PARPs) which are the attractive targets in various therapeutic areas such as cancer, antiviral, diabetes, and hormonal imbalance. The selective nature of tankyrase 1 and 2 inhibitors has created solid base to get dual site binders as they bind to induced adenosine binding site and nicotinamide binding site resulting in dual site inhibition. The present work describes the cheminformatics approach to find potential lead molecules as tankyrases dual site inhibitors through pharmacophore model by utilizing protein-ligand interaction fingerprints (PLIF) approach. The constructed pharmacophore model was used in virtually screening of ZINC and Interbioscreen database. Top ten hit molecules of virtual screening were subjected to molecular docking in order gain insights of key interactions at the adenosine and nicotinamide binding sites. The top hits were subjected to molecular dynamics simulation studies to gain deeper insights into probable mechanism of inhibition and stability of the complex. The top hit ZINC12973507 showed all the features required in key interactions at the active site of tankyrases and this hit molecule can be further explored as a potential drug candidate for dual site inhibition of tankyrases.